To advance the state of the art in translation process research, Toury (2004) requests the formulation of "probabilistic explanations in translation studies". This chapter develops these "conditioned statements" into a Noisy Channel Model of the translation process with the ultimate aim to predict "particular *modes of behavior*" by their observable traces in the user activity data (UAD). We first develop a Noisy Channel Model for the translation process and then present a number of research results that may serve as a basis for the formulation of observable behavioral units and of the latent states in a noisy translation process model. However, a large amount of research has still to be conducted before we might be able to get a complete picture of the various shades and complexities of the translation process.

### **1 Introduction**

The Noisy Channel Model (Shannon & Weaver 1949) has been very productive for solving non-deterministic problems in communication and computational linguistics. It is a mathematical formalization of communicative processes that underlies, among many other things, speech recognition (Huang et al. 1990), statistical machine translation (Brown et al. 1993) and the translation of a text from a source language to a target language. Statistical machine translation (SMT) models translation as a process in which a source text is decoded, thereby eliminating the noise (e.g. adjusting lexical and syntactic divergences) to uncover

Michael Carl & Moritz Schaeffer. Sketch of a Noisy Channel Model for the translation process. In Silvia Hansen-Schirra, Oliver Czulo & Sascha Hofmann (eds.), *Empirical modelling of translation and interpreting*, 71–116. Berlin: Language Science Press. DOI:10.5281/zenodo.1090954

the intended message (i.e. the translation). In automatic speech recognition, the speech signal is segmented, analysed and mapped onto a sequence of phones, which represents possible pronunciations of the words to be recognized.

In this chapter we develop a framework of the Noisy Channel Model for the translation process. In analogy with speech recognition, the translation *process* is modeled as a probabilistic sequence of behavioral observations, such as keystrokes and eye movements, which are emitted by underlying hidden processes. The aim of the noisy channel is to anticipate and generate the behavioral user activity data (UAD) and to uncover and understand the underlying hidden translation processes that are involved in the generation of the translation.

Just as for automatic speech recognition, the segmentation and quantification of the stream of events is a precursor also for decoding translation processes. A considerable amount of work has been invested in translation process research to define and investigate various kinds of units, measures and metrics that are suited to structure and quantify processing activities.

On the one hand, the final outcome of the translation process is a text (i.e. the translation) which is defined by the spatial/sequential order of the linguistic items that it constitutes. We may thus approach the translation process from a textual angle and investigate behavioral patterns that are involved in the production of particular words or phrases. Consequently, we will deal with text-based units of investigation, spatial areas of interest (AOIs1 ) which accumulate related behavioral UAD.

On the other hand, the translation processes can be considered a temporal sequence of translational events, which may be segmented into coherent chunks or behavioral units. For instance, pauses in the translation production process (i.e. gaps in the typing activities) have been associated with cognitive meta-activity and pause analysis has been proposed as a method to detect the amount of 'cognitive effort' in translation (e.g. Immonen 2006; O'Brien 2006; Lacruz et al. 2012). However, it is unclear what exactly the cognitive processes are that take place during typing pauses and it is an unsolved problem to determine what exactly makes pauses more or less effortful. In addition, recording of gaze data is required to 'fill' the typing pauses and to identify the specific motivation of a particular pause (Kumpulainen 2015: 47).

In both cases, a distinction can be made between early, automatised translation processes and later more time-consuming processes. A number of measures exist that are suited to describe early translation processes; these include fluent typ-

1This term was coined as a tool for the investigation of eyetracking data. It can be equally used to denote a textual area to accumulate different kinds of UAD.

ing activities and early eye movement measures such as first fixation durations. However, late translation processes are more complex, more difficult to describe and their traces in the process data are more varied and difficult to classify or identify than the early processes.

As suggested by Toury (2004), translation processes and behavioral observations are probabilistic in nature. In this chapter, we suggest a probabilistic framework to assess and integrate several findings from empirical translation process research. We first lay out the general ideas of the Noisy Channel Model in §2 and apply the introduced notions to the translation process research (TPR) terminology. We show that the Noisy Channel Model provides a powerful framework to formalize "probabilistic explanations in translation studies" (Toury 2004).

One of the essential requirements for a noisy translation process model is the fragmentation and quantification of the stream of UAD into meaningful segments. §3 discusses a number of attempts to segment the UAD into meanigful units, including production units, attention units and activity units. In the noisy channel model, these behavioral units are generated by underlying hidden translation states. Drawing on the monitor model, we make a distinction between early and later translation processes and argue that they represent different mental states. §4 provides a number of properties for these earlier and later translation states, the output of which can be measured in the behavioral UAD.

### **2 The noisy channel model in translation**

The Noisy Channel Model conceptualizes communication as a problem of decoding (Shannon & Weaver 1949), in which a transmitter sends a message*m* through a noisy communication channel. The receiver perceives a signal *o* as a noisy encoded version of the original message. In order to reconstruct the message *m*, the Noisy Channel Model assumes two factors: a language model *P*(*m*) which indicates the probability of the original message *m* and the conditional probability *P*(*o*|*m*) which quantifies the probability of the signal *o* provided we know the message *m*. The probability *P*(*o*|*m*) accounts for the noise that is added during the communication process. This component analysis can be achieved with the help of Bayes' theorem, which states that a conditional probability *P*(*m*|*o*) can be decomposed as *P*(*m*|*o*) = *P*(*o*|*m*) ∗ *P*(*m*)/*P*(*o*). Decoding makes use of the right side of this equation, ignoring the common denominator *P*(*o*). The noisy channel model is used to formalize a variety of different communication problems; in automatic speech recognition, *o* is an acoustic signal and *m* is the spoken message (Mylonakis et al. 2007). The model is also used for part-of-speech tagging,

in syntactical analyses, and in many other natural language processing (NLP) applications.

Figure 1: The noisy channel model for translation

In the context of SMT it is assumed that the target text (TT) corresponds to the message *m* and the source text (ST) is the signal *o* that we want to decode (see Figure 1). Given we know all the factors that are involved in the encoding process in *P*(*ST* |*TT* ) and we know the probabilities with which each single event occurs, we can reverse the encoding process based on Bayes' law as shown in equation 4.1.

$$P(TT|ST) \quad = \quad P(ST|TT) \* \frac{P(TT)}{P(ST)}\tag{4.1}$$

As each of the factors that contributes to a translation (i.e. the encoding and decoding) generates a large number of hypotheses, the Noisy Channel Model makes use of a search operator *arдmax* to retrieve the most probable translation among the many possible options.

$$
\overline{T}\overline{T} = -\operatorname{argmax}\_{} P(TT|ST) \* P(TT) \tag{4.2}
$$

The *arдmax* operator in equation 4.2 takes account of the fact that there can be many possible outcomes, but we are searching only for the most likely translation *TT*c. This operator produces, under optimum circumstances, the best re-

construction of the translation *TT*c based on observed source text (or sentence) ST.

$$P(ST|TT) \quad = \sum\_{l} P(ST|TT, \upsilon\_{l}) \tag{4.3}$$

Equation 4.3 demonstrates the possibility of including additional predictor variables*vi* in the Noisy Channel. Since the total probability of a sample space always amounts to 1 = ∑ *i vi* any number of additional variables can be introduced in this manner, so as to provide additional explanatory power to the computations.

Early approaches to SMT modelled the channel as a probabilistic translation dictionary (Brown et al. 1988). More recent SMT systems use many additional resources, such as phrase- or tree-based translation models; they encode sentences as lattices or confusion networks that enhance the noisy channel extensively. In order to integrate a large amount of features that might impact the decoding process, the Noisy Channel Model has been generalized as shown in equation 4.4, which can take into account any number of feature functions *fk* (*ST*,*TT*,*vk* ), which are weighted by a factor *λk* (Och et al. 2003). Each feature function *fk* (·) may represent a very different aspect in the decoding process and can be trained independent of other features. Its contribution to the overall outcome of the decoding process is ranked by a factor *λk* :

$$\widehat{TT} \quad = \operatorname\*{argmax}\_{k=1\ldots n} P(TT|ST) \approx \sum\_{k=1\ldots n} \lambda\_k \ast f\_k(ST, TT, \upsilon\_k) \tag{4.4}$$

We propose an adaptation of the Noisy Channel Model to model human translation and post-editing processes.

### **2.1 Probabilistic translation processes**

In an attempt to define the notion of translation universals, Toury (2004)requests the formulation of conditioned statements which would provide "probabilistic explanations in translation studies". Conditioned statements would predict "particular *modes of behavior* (or their observable results) … [based on] an array of *variables*, whose capacity to enhance (or reduce) the adoption or avoidance of a particular behavior would be verified empirically" (Toury 2004: 24). The most general format of such a conditioned statement according to Toury would be as

follows:

If 1 and 2, and 3, and … ∞, then there is great likelihood that X […] where the numbers (1, 2, 3, … ∞) stand for the different variables which may have an effect on the selection of a translational behavior. (p. 26)

In terms of the notion introduced above, Toury's conditioned translation statement can thus equivalently be expressed as a set of conditional probabilities in the form *P*(*xi* |*v*1 ,*v*2, . . .,*vm*) where *xi* stands for a particular predicted translation behavior and the set *V* = {*v*1 ,*v*2, ...,*vm* } contains predictor variables which have an effect on and explain — to a certain extent — the observed behaviour *xi* in a probabilistic manner. Even though there might be many different modes of translation, we assume that the translation process consists of a finite inventory of behavioral patterns *X* = {*x*1 , ..., *xn* } and we assume — in contrast to Toury that for each of the observations *xi* ,1≤*i*≤*n* there exists only a finite set of predictor variables *vj* ∈ *V* that has an effect on *xi* . The observed translation process *X* can then be formalized as a sequence of possible behavioral patterns *x*1 , *x*2 . . . that are conditioned by a number of predictor variables*v*1 ,*v*2 . . . . The most likely (explanation for) translation behaviour *X*b can thus be computed in a similar way as the most likely translation *TT*c.

The general idea in this model is that the value of a dependent variable (*X*) is related to a set of independent variables (*V* ) through a function (*F* ). Given the translation UAD, we learn the function (*F* ) to minimize the error (also known as loss (*L*)) in prediction (*X*b) of the variable *V* .

$$\text{minimize } L(X, \dot{X}) \text{, where } \quad \dot{X} = F(X, V) \tag{4.5}$$

### **2.2 Latent translation states**

As an illustration of a probabilistic conditioned statement, Toury (2004) discusses a made-up example in which he illustrates a hypothetical effect of experience and fatigue on whether translational processing will be applied to small or low-level textual-linguistic entities. In this example, the level of textual-linguistic entities that a translator works with would be represented by the dependent variable *X* whereas the explanatory (or predictor) variables *V* represent the experience and fatigue of the translator which may have an effect on the choice of the translation unit.

Toury is not very consistent in his usage of the terms "translation modes" and "translation behavior". Surprisingly, for him the translator's behavior "is not really observable in any direct way" (Toury 2004: 26). He nevertheless mentions possible forms of translational behavior which include all kinds of "regularities [that] can be found on every level, from the individual act of translation […] to the overall notion of translation", under which he also subsumes translation universals, i.e. structures in the translation product. We will come back to this issue in the conclusion.

On the one hand, Bernardini (2001: 241) points out, "an understanding of translation [processes] […] is not derivable solely from an analysis of the final product". On the other hand keylogging and eye-tracking technologies give us today the possibility to directly observe and investigate translation behavior and empirically assess the granularity of the chunks that a translator works with. Accordingly, we conceptualize the translation process as successive intermediate versions of a text (i.e. the emerging translation), which are the direct consequences of translation behavior. Most important in this process are obviously the keystrokes which are the direct causes for text modifications.

As an extension to Toury's model, we assume that the behavioral patterns are triggered through internal (latent) states in the translator's "black-box". Using EEG and fMRI technologies we may be able to investigate and measure these latent states directly through experimental equipment in the near future (Annoni et al. 2012). Currently however, Think Aloud Protocols (TAPs) and introspection are methods used to assess the hidden (or latent) states in the translation process. Two of the main goals of TAP research are (1) to describe translation problems, and (2) to isolate strategies and translation procedures. According to Lörscher (2005: 599), the "data [that are collected through TAP] are interpreted as (observable) indicators of (unobservable, mental) translation strategies" which, for him, represent the basis for the formation of hypotheses regarding the mental translation process. Based on the collected data, Lörscher (1991) describes five basic translator types which differ with respect to how much the solution of a translation problem is automatized, whether the translator requires search, whether a translation problem is decomposed into smaller parts, and to what extent the translation problems are consciously accessible and can be verbalized. Lörscher (1991: 280) finds that "[w]hen several [translators] are faced with a problem X, many or most of them employ similar or the same types of strategy".2 However, findings like these remain to be quantified and scrutinized for their predic-

2While this looks similar to Toury's conditioned statement, the "problem X" would here be a predictor variable, while the dependent variable "types of strategy" is a latent translation state.

tive value. For Krings (1986), TAPs have only a restricted validity. He cautions that "although verbal data do give evidence of mental processes, they cannot be claimed to be isomorphic with those processes" (Krings 1986: 264).

Figure 2: A hidden Markov Model

The translation model suggested by TAP analysis can be formalized as a Hidden Markov Process in which a number of interconnected "hidden" states emit observations with a certain probability. Figure 2 outlines the idea of a hidden Markov model, where the two hidden states (Low and High) emit possible observations (Rain or Dry) with a certain probability. A hidden Markov model can consist of a large number of hidden states and emit many different observations. The hidden states are organized in the form of (possibly completely connected) recursive networks and transition probabilities that indicate the likelihood with which one state follows another. A number of efficient algorithms exist to learn transition and emission probabilities from data and to compute most likely sequences of observations.

### **2.3 Early and late stages of translation states**

de Groot (1992), Hartsuiker et al. (2004), Lopez & Resnik (2009) and Schaeffer & Carl (2013) assume that entries in the mental bilingual dictionary consist of nodes that link the lemmas, concepts, word forms and syntactical information between the two languages. The nodes are linked to all words that exhibit correspond-

ing features in a language-independent fashion. They are, however, specific to particular language combinations as not all languages always realize the same morphosyntactic and semantic aspects in the same way.

The models predict that the more nodes overlap between the source and target language words and structures, the less time it takes to retrieve associations and to generate translations. This unchallenged translation process is to a large extent a subliminal process.

The translation model by Schaeffer & Carl (2013) posits that the translation process is *recursive* given that translators often switch back and forth between source and target text in order to examine both texts for interpretive resemblance. In doing so, translators are primed by either the source or the target text, allowing them to register and analyze the resemblances in both texts. This view is in line with the monitor model (Tirkkonen-Condit 2005) according to which an automatic default translation procedure is interrupted when a problem occurs and triggers conscious translation processes.

Figure 3 visualizes an unchallenged translation process. It shows a relatively undisturbed translation progression in which an English source sentence "All of his victims were old weak woman" is translated into Danish "Alle hans ofre var aeldre svagelige kvinder" on the left and right axes respectively. Translation activities are depicted in the graph on a timescale, from ms 206.000 to 215.000. The overall translation of the eight words takes approximately 9 seconds. The figure shows keystrokes (insertions and deletions), gaze fixations on the source text (blue boxes with dots) and gaze fixations on the target text (green boxes with diamonds).

Figure 3: Example of an undisturbed translation progression

Some of the measures for the translation segment in Figure 3 are shown in Table 1 and explained as follows:

• *FFDur*: first fixation duration on the source word (blue rectangle)

Table 1: Behavioural measures for smooth translation activities.

The segment in Figure 3 is chararacterized by relatively few fixations on the source and target words and relatively short total reading times. There is a short delay between the reading of a source word and the production of the translation (i.e. the eye-key-span; see Schaeffer & Carl 2016, Schaeffer & Carl 2017 [this volume]). Only the translation of "weak" and "women" required longer reading times, perhaps due to unusual character combinations in the Danish translations.

Figure 4 shows an excerpt from an English → Chinese translation session, with much more complex patterns of ST and TT reading behavior, repeated regressions, re-reading, backtracking, deletions, revisions, etc. The production of this translation segment of 17 words took approximately 100 seconds, which is almost 5 times longer per word than the Danish translation in Figure 3. The ST segment "the extra green mile" was read at least seven times, four times during an orientation phase between seconds 210 and 240 and then again three times during translation drafting.

Figure 4: Progression graph with complex patterns of monitoring behavior

Table 2: Measures of challenged translation processes

Table 2 lists the behavioural measures for the translation segment which was more challenging. In total, there were 20 and 24 fixations on the words "extra" and "green", respectively. The first fixation durations *FFDur* make up less than 5% and 3%, respectively, of the total reading time for these words, indicating that most of the translation effort is related to later processes, such as source text integration or formulation of a translation hypothesis. In this example it seems that much effort was required to understand and/or formulate a first translation hypothesis for the phrase "extra green mile" since most of the reading occurred before the translation was typed.

The words "the", "extra" and "miles" remain untranslated (*Munit = 0*); only a Chinese translation of "green" was produced and aligned.3 The relatively higher

3Dashes '—' on the right Y-axes in the translation progression graphs indicate non-translated or non-aligned words for which there are no correspondances in the translation.

*HTra* values indicate that translators have produced more different solutions for these words than for the translation of "green".

The available eye movement measures seem to be well suited to capture unchallenged translation processes. However, existing measures are not well suited to describe the more complex reading patterns occurring during the later stages of challenged translation, because they either describe early processes (e.g. first fixation duration or first pass reading time) or they do not capture the time course of the late processes and very few measures describe the interrelationship between reading and writing activities (see also Schaeffer & Carl 2017).

Table 3 summarizes some of the existing measures. They will be explored in more detail in §4. A distinction is made between reading measures which capture gaze activities, writing measures which describe typing processes and R&W measures which describe how reading and writing are coordinated. These measures refer to sequences of the source and/or target texts, so-called Areas of Interest (AOI). AOIs are typically single words, phrases or sentences, and are characterized by the accumulated UAD as well as their linguistic and other annotations. The first pass reading time, for instance, is the sum of fixation durations on a word (or another predefined text segment) from the first fixation before the eyes leave the AOI again. The word production time (*Dur*, cf. page 96) is the total time needed to type a word (i.e. a translation), including all its possible revisions. R&W measures shown in Table 3 can take values which may indicate early or late processes.

Table 3: Measures of the translation process

### **2.4 A noisy translation processes model**

A translation process model taking into account hidden states of early and late processes is depicted in Figure 5. The model resembles a Hidden Makov Model (HMM) in Figure 1 which consists of four levels of description. In the center is the *Translator* who is constrained by a number of factors and who produces a sequence of behavioral patterns which lead to the final translation product.

The *Predictors* are a vast number of variables which are likely to play a role in the translation process and which originate from an enormously heterogeneous field, including cognitive, linguistic, cross-linguistic, or textual, communicative and socio-cultural domains (Toury 2004). Other researchers (e.g. Risku 2014) also mention environmental conditions, including physical, geographic, economic, political and demographic aspects which might play a role in the translation process. The *Source Text* is another crucial predictor which will determine the characteristics of the target text.

Figure 5: Observations, predictors and hidden variables in the noisy translation process model

The *Translator* is modelled as a network of hidden states which implement the actual translation processes. In contrast to earlier hierarchical-stratificational models of translation (Nida 1964; Seleskovitch 1975), it is now generally accepted that there are states of early, automatic processes and states of more deliberate, strategic processing. Hönig (1991), for instance, proposes a translation model which establishes a distinction between uncontrolled, associative translation competence (i.e. unconscious early translation processes) and a controlled workspace in which micro and macro strategies are stored. The associative translation competence corresponds to subliminal priming mechanisms, while the monitor processes occur at a later stage as they require extensive conscious effort.

The output of the model in Figure 5 has two levels of observations: *product observations* capture the changes in the translation product, i.e. the sequence of intermediate texts that are produced during the translation process. The final translation product is the final outcome in a series of successive intermediate text snapshots that emerge during the translation process and the translation process can be approximated by comparing the successive intermediate text snapshots. These observable textual changes are direct consequences of translators' activities which can be traced through logging technology.

Objective UAD such as keystrokes, mouse clicks, eye movements and other behavioral data can be recorded with keyloggers, eye-trackers and other tools, but the collected UAD needs be segmented into meaningful *behavioral patterns*. However, it is neither obvious how keystrokes and gaze data should be segmented, nor is it uncontroversial what the latent states are which emit those patterns.

The HMM in Figure 5 suggests that:

In §3 we will be concerned with the description and analysis of the behavioral patterns. The stream of UAD can be fragmented into segments of behavioral units which are suited to describe the translation process. A Production Unit (PU), for example, is a coherent sequence of keystrokes where the lapse of time between successive keystrokes is below a given threshold, e.g., 1 sec. A PU can

thus contain a single or a large number of keystrokes irrespectively how many words are produced.

In §4 we argue that different hidden states can be related to different temporal aspects in which they are triggered. We discuss properties of earlier and later translation activities and hidden states in more detail.

### **3 Patterns of translational behaviour**

This section discusses several approaches to fragment the UAD into sequences of behavioral patterns. Such patterns fragment the stream of translation activities on a temporal scale. We discuss units which capture gazing and typing data. In contrast to the behavioral data accumulated in textual AOIs, the UAD within the behavioral patterns may relate to several different textual items that may be at distant locations from each other. While there is a large repository of linguistic terminology to describe textual elements in AOIs — such as PoS tags, linguistic functions etc. — there are only very few approaches which fragment the translation process data and little work has beed done to describe these units.

### **3.1 Production units (PUs)**

Carl et al. (2016); Carl & Kay (2011) define Production Units (PUs) as sequences of coherent keystrokes, where the pause between any two successive keystrokes is less than 1 second. A pause of more than 1000ms constitutes a PU boundary. PUs fragment the stream of translator activity data into sequences of coherent typing and pauses that separate them. In contrast to a micro unit (see page 103), a PU may stretch over several words, while a micro unit is defined as the flow of continuous typing that contributes to the production of one target word. A PU that stretches over *m* words would thus be split into *m* micro unit, where each produced word 1...*m* is assigned its share of keystrokes. A word can be associated with several micro and production units, depending on how often it has been revised. A PU contains, among other things, the following information:

Singla et al. (2014) investigate to what extent post-editor profiles can be identified based on the information contained in PUs. They use data from five posteditors producing together 120 translations sessions which is contained in the LS14 study4 . They test several machine learning techniques but find that "multilayer perceptron" and "classification via regression" perform best for this task. Using 10-fold cross validation for classification, they achieve 46.48% accuracy to identify post-editors which exceeds by far the baseline accuracy of 20% which is based on guessing a post-editor by equal chance among the five participants.

Aziz et al. (2014) analyze PUs of post-edited texts, to investigate whether and how the properties of PUs are related to features of the sentences they appear in. Their investigation uses the CFT13 dataset5 that was generated with the CAS-MACAT workbench (Alabau et al. 2013). PUs contain post-editing information about number of insertions, deletions post-editing time etc. Aziz et al. (2014) add further information to generate high dimensional feature spaces with nearly 100 features. The additional information included POS tags, named entities, chunk labels, and labels of semantic roles. The information was separated into PU level features and sentence level features "such as the number of tokens in the sentence, the number of different phrases or the number of predicates and their arguments, which could indicate that the overall sentence is complex" (179).

The authors use Principal Component Analysis (PCA) to visualize the high dimensional feature space and provide a detailed analysis of the data. Aziz et al. (2014) find that a correlation between sentence length and post-editing time can be observed mainly in cases of low post-editing activities. They find, for instance, that "PUs involving verbs are slightly more time-consuming, while PUs related to nouns require slightly more typing" (Aziz et al. 2014: 189). On the one hand, it is therefore "possible to decouple sentence length from the difficulty of each PU in terms of how time-consuming and how many edits (character level insertions and deletions) it requires." (Aziz et al. 2014: 189) On the other hand, a pause analysis becomes difficult, since "the pause prior to editing correlates very poorly to the character-level edits performed." (Aziz et al. 2014: 187) It is unclear

4The data can be downloaded from http://sourceforge.net/p/tprdb/svn/HEAD/tree/LS14/

5The data can be downloaded from http://sourceforge.net/p/tprdb/svn/HEAD/tree/CFT13/

why a pause occurs and whether it is related to the successive typing events, as manifested in the PUs. This is supported by their finding that "HTER correlates better with time and typing related to individual PUs than to cumulative sentence level indicators" (Aziz et al. 2014: 187)

With respect to editing times, the authors find the following relations:

(Aziz et al. 2014: 188) further find that "there is very little correlation between the length of a sentence and how time-consuming individual PUs are". In other words, post-editors process sentences in smaller units so that the post-editing duration does not necessarily depend on properties of the whole sentence, and hence sub-sentence features may provide more informative cues about actual editing effort than, for instance, sentence length. It is unclear whether and to what extent the findings for post-editing carry over to from-scratch translation.

Schaeffer & Carl (2016) attempt to predict concurrent ST reading and TT typing during from-scratch translation production. Their investigation is based on the assumption that "instances of concurrent reading and writing during translation are indicative of automatic processes and shared representations".They investigate concurrent activities in PUs using several possible predictor variables.They find that:

• more experienced translators are more likely to show concurrent R&W activities

The strong impact of the syntactic similarity on concurrent processing underpins their initial hypothesis that processes are likely to be more automatic when the ST and TT word order is similar, as in this case primed, shared syntactic representations may more easily serve as the basis for TT production.

### **3.2 Attention units (AUs)**

In order to compare the cognitive flexibility and processing automaticity of professional and student translators, Hvelplund Jensen (2016) suggests to segment the translation activity data into attention units (AU).

Following Baddeley (2007), Hvelplund Jensen (2016) argues that, on the one hand, cognitive flexibility is linked to planning, problem solving and decision making and involves the ability to focus and switch attention, or to divide attention simultaneously into several subtasks. Hvelplund further states that a translator with good cognitive flexibility will "focus attention for precisely as long or short a period of time as is necessary only to those subtasks which are relevant to the successful execution of the translation task" (Hvelplund Jensen 2016: 153).

On the other hand, based on TAP studies (e.g. Jääskeläinen & Tirkkonen-Condit 1991) it has been suggested that professional translators rely more on automatic processing than students. Translators' automaticity is, thus, closely related to experience.

In order to assess these hypotheses on the basis of translators' UAD, Hvelplund Jensen (2016: 157) operationalizes the notion of *attention unit* (AU) in the following way:

an AU is defined as uninterrupted processing activity allocated either to the ST (ST gaze activity), the TT (TT gaze activity and/or typing activity) or to the ST while typing (ST gaze activity and concurrent typing). Transitions to and from an AU indicate shifts in processing activity, and the point in time at which the transition occurs is used to identify the end of one AU and the beginning of the next AU.

He thus defines five AUs based on the following activities:

**AU5:** Typing

While the cognitive flexibility is computed based on the durations of the AUs, the automaticity of the process is reflected in the pupil size where smaller pupil sizes indicate relatively less cognitive load than larger ones. The pupil size for a AU was calculated as an average of all its gaze samples and a latency effect of 120 ms was factored into the calculation.

Based on an evaluation of the KTHJ08 data as shown in Table 4 (see page 97), Hvelplund Jensen (2016) finds that:

Further, in order to assess the translation process flow, Hvelplund counts all the transitions between any two successive AU labels, separately for professional and student translators, and stores them in a 5×5 transition matrix. He compares the two matrixes and observes that experienced translators shift from AU1 (ST reading) in 65.5% of the cases to typing activity (either of AU2, AU4 or AU5) while student translators do this only in 52.2% of the cases. Student translators switch to AU3 more often than professionals, which suggests that students aim more often at confirming meaning hypotheses (reflecting some kind of uncertainty), rather than allocating the cognitive resources directly to TT typing once a meaning hypothesis has been established.

### **3.3 Actvity units**

Not unlike Hvelplund Jensen (2016) AUs, Carl et al. (2016) suggest to fragment the activity data into seven different types of segments with the following labels:

**Type1:** Reading the source text (ST) **Type2:** Reading the target text (TT) **Type4:** Typing activity **Type5:** Typing while reading ST **Type6:** Typing while reading TT **Type7:** Typing while reading ST and TT6 **Type8:** No activity recorded

Type1, Type2 and Type4 are basic translation activities. Type5 to Type7 take into account that source and target text reading can occur concurrently with typing, and a Type 8 is assigned to segments if no activity is logged for longer than a given threshold. Figure 6 shows the segmentation of a translation segment into AU units. The data is identical to that in Figure 3 5 but AU boundaries are marked.

Figure 6 shows a long ST reading activity (Type1, in blue) of approximately 30 seconds, between seconds 208 and 238, followed by a number of shorter pauses (Type8, in black), TT reading (Type2, in green) and typing activities (Type4, in pink) etc. These segments describe exhaustively the translation process and the properties of the sequence might be significant for certain types of translators and/or translation strategies.

In order to assess to what extent the profiles of machine-translation posteditors can be detected from the labels of the AU, Singla et al. (2014) investigate units of Type4 and Type8 (i.e. typing and pauses) of five post-editors with different amounts of experience. They use data from 120 translations sessions which are extracted from the LS14 study7 and subdivide Type 4 and Type 8 units into five categories based on their durations. They compute a trigram language model of activity sequences for each post-editor and compute a transition matrix which is filled with the perplexity scores of each post-editor's language model on the

6 In more recent work, this type of unit is decomposed into units of type 1,2,4,5,6 or 8

7The data can be downloaded from http://sourceforge.net/p/tprdb/svn/HEAD/tree/LS14/

 Model for the

translation

 process

4 Sketch of a Noisy Channel

Figure 6: Translation session fragmented into CU units

other post-editor's activity sequences. A discriminative classifier is then used to cluster post-editors into two classes on the assumption that the events that make up the translation process provide enough information for the individualization of post-editor profiles.

Singla et al. (2014: 56) find that "experienced post-editors produce similar kinds of activity sequences in contrast with the activity sequences of inexperienced post-editors". They also notice that post-editors with a similarly negative attitude towards post-editing tend to have similar activity patterns.

Martínez-Gòmez, Aizawa, et al. (2014) use a subset of 204 sessions from the data shown in Table 4 that is annotated with information about translator experience and certification: 99 of the 204 sessions were produced by 47 non-certified translators, and 105 sessions were produced by 47 certified translators. They report that:

translators engage 14% of their time in source text reading, between 17% to 37% in target text reading, between 35% to 42% inserting characters and 4% deleting characters. Certified translators spent significantly larger proportions of time in target text reading and target text typing than non-certified translators. The most common translation activity was the concurrent combination of "source text reading", "target text reading" and "target text typing", which occurred around 45% of the time for non-certified translators and 65% of the time for certified translators. (Martínez-Gòmez, Aizawa, et al. 2014: n.p.)

In an extension of this experiment, Martínez-Gòmez, Minocha, et al. (2014) use the same data to recognize translator expertise, based on the assumption that "Translators have different perceptual and motor activities, depending on their level of expertise." They compare two methods to assess this hypothesis, one based on the AUs, and another one using unsupervised machine learning techniques with a view to discover regularities in the logging events and to reveal latent activities, that would otherwise not be detected.

For the unsupervised learning method, each log event (fixation and keystroke) was enriched with 31 additional features that were extracted from the immediate context, such as the number of insertions, deletion and fixations within the past and the next 10 events, together with the time offsets from the current event. The information was stored in the form of vectors which were then classified using a k-means clustering method (3 to 8 classes). Tri-gram language models were built from the sequences of cluster labels, and random forests used to predict translator expertise, such as whether the user is a certified translator or not (binary classification), his/her years of training (regression) and years of experience (regression).

Martínez-Gòmez, Minocha, et al. (2014) report an error reduction in the recognition of certified translators, and moderate but significant error reductions in the recognition of years of experience, as compared to a baseline. Best results were obtained with the unsupervised technique. They also report that CU unit of type 5 (i.e concurrent ST reading and typing) is more likely for certified translators than for non-certified translators.

### **3.4 OST units**

Another approach to fragmenting the process data was suggested by Nitzke & Oster (2016). They manually annotate the activity data into two main categories, orientation (O) and revision (R) with five sub-categories:

4 Sketch of a Noisy Channel Model for the translation process

Figure 7: Annotation of OST units

With five sub-classes the annotation schema is less complex than the CU activity units - and much coarser grained. The translator activity data of 406 segments has been manually annotated into 985 segments, which is on average slightly more than two OST units per segment.

In an attempt to automatically detect OST units, Läubli & Germann (2016) segment the process data into fragments of 3 seconds, and assemble all process events (keystrokes, mouse clicks, ST fixations and TT fixations) for each segment in a vector of observations. Similar to the method used by Martínez-Gòmez, Aizawa, et al. (2014), the observation vectors are then classified with a k-means clustering method and a Hidden Markov model (HMM) is trained on the sequences of cluster labels and observation vectors. The assumption is that the cluster labels represent the underlying states of the OST annotation (orientation, revision, and, as an additional state, also pausing) where each state produces randomly an observation. The transition probabilities in the HMM and observation probability densities are then trained based on the available data. The aim of the model is to yield the most probable label for each observation, taking into account (i) the feature values (dimensions) of the current observation and (ii) the label assigned to the preceding observations. In a final step the cluster labels are mapped on the three OST labels: orientation, revision and pause. The authors show that the system reaches as high an accuracy to predict the times spent on orientation, revision and pause as some of the human annotators.

### **3.5 Conclusion**

This section summarizes different methods of segmenting the UAD into successive chunks. Depending on the available logging data, most of the segmentation methods make use of cues in the data, such as text production pauses and/or the location of the gaze data on the source or target text to define segment boundaries. An exception is the segmentation method by Läubli & Germann (2016) who segment the UAD into chunks of 3 seconds duration. With the exception of OST units described in §3.4, all segmentation methods work fully automatically.

The reported investigations show that some segment properties are typical for different translator profiles and degrees of translator expertise. They are are also indicative of various translation problems.

The research discussed in this section can be characterized as instances of probabilistic translation modelling as discussed in §2.1 and equation 4.5 on page 76. Models are sought which predict behavioral patterns based of a number of different predictor variables. Linguistic features of the source text are investigated with respect to their effect on production times and revision behavior, patterns of reading and writing are related to cognitive models of the translator, such as translation expertise, and different translation techniques, machine translation and from-scratch translation are assessed in relation to translation effort.

Pause analysis is perhaps the most common approach for the analysis of behavioral patterns. In pause analysis it is assumed that longer pauses between successive keystrokes signal higher cognitive effort. O'Brien (2006) analyses keystroke pauses in post-editing and suggests that analyzing pauses is a useful indicator of cognitive effort in post-editing. Immonen (2006) finds that in translation, pause length is higher at word and clause boundaries. Lacruz et al. (2012) introduce average pause ratio as a metric to establish a relationship between pauses and cognitive effort in post-editing.

However, to obtain a more complete picture of the translation process, we ought to investigate the translators' "black box" in more detail. In the next section we will therefore investigate properties of the translators' hidden states, which, according to the Noisy Channel Model in §2.4 emit behavioral patterns.

### **4 Hidden translation states**

It is unclear how many hidden translation states can or should be distinguished that participate in the translation process. However, a distinction can be made between states which are triggered through early priming mechanisms and other

more time-consuming and late(r) states which involve more cognitively demanding problem solving strategies. The TP model suggested in Figure 5 distinguishes these states into "early" and "late" states.

Priming is an unconscious mechanism that is based on the implicit memory of a first (source) stimulus which carries over to a subsequent, target stimulus and which has an impact of the execution of a following task. It has been shown that bilinguals, and therefore also human translators, use implicit memories during language production. Priming effects exist between stimuli in different modalities, such as visual and verbal. They are, however, stronger if source and target stimuli are in the same modality, e.g. within written language. Priming effects can be observed in translation and in post-editing of machine translation output (PEMT), but — as we will show — the effects are more noticeable in PEMT, presumably due to the fact that priming effects are generally stronger within one language (i.e. the MT output and final translation) than between two languages (Pickering & Ferreira 2008).

The degree of similarity between source and target items has an effect on the strength of the priming effect – the greater the similarity, the stronger the priming effect. Priming facilitates and simplifies translation. Priming effects exist for the choice of words as well as for word order. Hvelplund Jensen (2009) and Ruiz et al. (2008) report shorter ST reading times in translation if the word order in the ST is identical with the word order in the TT. Schaeffer, Dragsted, et al. (2016) report longer reading times for words with more possible choices than for words with fewer choices. This result is in accordance with Campbell's Choice Network Analysis (Campbell 2000): The more choices translators have in the selection of a translation, and the more complex the decisions are that they have to make, the more difficult the translation will be. Simpler translational decisions often lead to identical results while more variation in the traslation often implies difficult more difficult decisions.

As shown in Figures 3 and 4, translation process data encodes traces of early, automatized and later translation behavior. Automatised processes occur quickly and leave their traces early on, while later, more time-consuming processes are likely to involve more conscious problem solving activities.

A noisy channel model of translation as depicted in Figure 5 takes into account various kinds of hidden processes which ought to explain and generate the traces in the observed UAD. This section summarizes a few constraints of the hidden states, related to the observable output of early and later processes.

A large amount of research exists that investigates conscious processes in translation (e.g. Jääskeläinen & Tirkkonen-Condit 1991; Lörscher 2005). Accord-

ing to Gutt (1989), the translator's task is to recode the source text into a target language text in such a way that interpretive resemblance in regard to explicatures and implicatures of both texts is achieved. In order to examine interpretive resemblance, translators consciously apply meta-cognitive monitoring processes. Gutt's theory builds on relevance theory (RT, Sperber & Wilson 1995), which posits that linguistic forms encode semantic representations that are recovered using unconscious, automatic decoding processes. As a pragmatic theory of communication, RT seeks to explain the inference procedures that build on the automatic encode-decode mechanism and on which successful communication relies. The distinction between the process of encoding-decoding messages and the process of making inferences from evidence coincides for Blakemore (2002) with the distinction between semantics and pragmatics: linguistic semantics provides logical forms which are taken as input by pragmatic inferences constrained by the principle of relevance.

This section aims at giving empirical evidence for the existence of early and late translation processes. In §4.1 we describe the experimental material that much of the successive sections rely on. In §4.2, we investigate linguistic parameters that have an effect on the word production duration. Production duration is a possible indicator of translation difficulty and the amount of priming and more time-consuming translation strategies that went into the production of a translation. We show that, among other parameters, the number of possible translations for a word is a strong indicator of translation difficulty, which has an impact on early as well as late translation processes. In §4.3 we have a closer look at syntactic properties of lexical variation in the translation product in from-scratch translation and in post-editing. Finally in §4.4 we discuss revision behavior, which accounts probably for the latest of the translation processes.

### **4.1 Experimental Material, Measures and Metrics**

Table 4 gives an overview of the size and number of texts. A total of 336 target texts (TTs) with a total of 48.295 target language tokens (TT Tok) were produced from six different English source texts (ST) into four target languages, Danish (da), German (de), Spanish (es) and English (en). The English TTs resulted from a copying task (C), English to English, whereas the other texts were either postedited (P) or translated (T). The translations were produced by 95 translators over a period of 38 hours (*FDur*). The column *KDur* shows the accumulated keying time, excluding production pauses of more than one second. Note that the ratio of keying time (*KDur*) vs. total production time (*FDur*) is much smaller for

Table 4: Annotated data for the syntactical entropy study

4 Sketch of a Noisy Channel Model for the translation process

post-editing than for from-scratch translation, and even less in the copying task. Danish translations were only produced for three texts (1-3). The column *Part* indicates the number of different participants involved in each translation study.

From the logs of these sessions, a number of features were extracted, (cf. Carl et al. 2016), among others:

In order to assess the literality of a translation, Carl et al. (2016); Carl & Schaeffer (2016) introduce a literality metric which measures the similarity of a source text (ST) and its translation, the target text (TT), along the following three criteria:

Literality criterion (2) is met if each word in the ST corresponds to exactly one TT word and vice versa, while criterion (1) is realized if the translation equivalents occur in the same order in the ST and in the TT. These two criteria are represented by an integer value, referred to as *Cross* and relate to the amount of crossing word alignments (inter-lingual alignment distortion). A one-to-one correspondences results in a *Cross* value of 1, and this value grows (negatively or positively) with the distance between the aligned words. Approximately 40% of all words in the TPR-DB (English STs) have a *Cross* value greater or smaller than 1 (Schaeffer, Dragsted, et al. 2016: 190).

In order to assess literality criterion (3) we use a corpus of word-aligned, alternative translations and measure the entropy of the translation realizations. This measure is referred to as word translation entropy *HTra*. Approximately 90% of all words in the TPR-DB (English STs) have more than one translation alternative, and thus a value *HTra* > 0 (Schaeffer, Dragsted, et al. 2016: 190).

### **4.2 Production duration in translation and post-editing**

The reduction of translation duration (the increase of productivity) is a driving force for much of the technological development of machine translation (MT) and for post-editing of machine translation (PEMT). While it has been shown in several places that PEMT is often quicker than from-scratch translation (Plitt & Masselot 2010; O'Brien et al. 2014), it has not often been investigated what the possible determining factors, and what the impact for on the translation product are.

To test which properties of the text might have an impact on the translation duration, we analysed six English texts from studies BML12 and SG12, (see Table 4). We extracted 15,313 ST and 15,568 TT words that were translated into Spanish and German by 32 and 24 translators respectively and they were also post-edited into Spanish and German by 32 and 23 translators, respectively. The

data was analyzed using R (R Development Core Team, 2014) and the lme4 (Bates et al. 2014) and languageR (Baayen 2013) packages to perform linear mixed-effects models (LMEMs). To test for significance, the R package lmerTest (Kuznetsova, Christensen, & Brockhoff, 2014) was used, which implements ANOVA for mixedeffects models using the Satterthwaite approximation to estimate degrees of freedom. The final model included participant, ST token, text and target language as random variables. The predictor variables were ST token frequency (*Prob1*), word length of the ST token in characters (*LenS*), *Cross* and *HTra*, in addition to task, i.e. post-editing and translation, as an interaction with both *Cross* and *HTra*. The dependent variable, production time per word (*Dur*) was log transformed, because it was not normally distributed. Data points which were more than 2.5 standard deviations below or above a participant's mean were excluded (3%). All effects were highly significant (all *t* > 3 and all *p* < .001).

The translation duration *Dur* indicates the production time for a translation. It is also an indicator of earlier and later processes: the more time is needed to produce a translation, the more likely will the translator be involved metacognitive reasoning. As shown in Figure 8 the production duration dependes on a number of additional factors.

Figure 8 shows the effect of *Cross* and word translation entropy (*HTra*) on word production time *Dur*. The Figure shows that post-editing is much quicker for words which have small *Cross* and *HTra* values. Post-editing may take as long as from-scratch translation if the MT output is modified (i.e. many different variants are produced) and/or for large *Cross* values.

It is possible that MT systems produde more acceptable translations for segments in which the word order is similar (i.e. *Cross* values are low) than for segments in which a large amount of syntactic reordering is required. In turn post-editors would need to produce less modifications for translations with low *Cross* values which would explain why post-editors take less time to produce these words as compared with words which have a very different position in the TT, in relation to the ST.

### **4.3 Variation in translation and post-editing**

The amount of different translations that are possible for a word (*HTra*) has a strong effect on production time. In this section we investigate this phenomenon in more detail. Post-editors seem to be less creative than translators; often, they do not modify the MT output which leads to fewer variants in the translation product than during from-scratch translation.

Figure 8: The effect of source text frequency (*Prob1*), ST word length in characters (*LenS*), word translation entropy (*HTra*) and word order differences (*Cross*) on ggproduction time (*Dur*) and observed translation variants for post-editing (P) and translation (T).

Čulo et al. (2014), for instance, describe a study in which 12 professional translators and 12 translation students translate or post-edit six texts from English (L2) to German (L1). Čulo et al. (2014) discuss the following MT output in detail:

EN : *In a gesture* sure to rattle the Chinese Government

DE : *In einer Geste*, die die chinesische Regierung wachrüttelt

The German translation "*In einer Geste*" is understandable but not idiomatic. It is a literal one-to-one translation – according to criteria 1 and 2 above – which was generated by an MT system but which was rarely changed by the posteditors. However, a great variation of different idiomatic versions was found in human translations of the same text segment. Human from-scratch translations for the above example include: "Als Geste", "Es ist eine Geste", "Mit der Absicht", "Als Zeichen des Widerstandes" and "Mit einer Aktion". The eight translators who translated this text produced seven different versions, while seven posteditors only came up with three different versions. The example clearly shows that translators are more creative, resulting in more diverse translation solutions and thus high *HTra* values, while post-editors are more heavily primed by and biased towards the solutions generated by the MT system which results in low *HTra* values but also faster production times. Note also that the translation *In a gesture* ↔ *In einer Geste* can be aligned word-by-word, which is not the case for most of the from-scratch translations.

Tightly connected to the phenomenon of interference is the amount of variation in translation solutions. Figure 9 shows word translation perplexity from English to German and English to Spanish for different word classes (PoS tags, see Table 5, below). The texts were extracted from the SG12 and BML12 studies (see Table 4) and contain approximately 800 source text words. The degree of translation variance can be measured as perplexity: an even distribution of several realised translations (e.g. all translators generate a different translation) leads to high perplexity values, while an uneven distribution (i.e. many translators generate the same translation) does not.

The values for post-editing and original translation are indicated. Some PoS tags, such as e.g. JJS (superlative e.g. "largest", "least"), NNP (proper names), CC (conjunctions) only produce a very small number of translation alternatives (low degree of perplexity). Other PoS tags, such as e.g. RP (particles), VBN (participles) exhibit more variation in the target text. In any case, the degree of word translation perplexity in post-editing is always lower than in translation from scratch. As pointed out in §4.3, this is presumably due to the fact that MT output

Figure 9: Priming in translation (TRA) and post-editing (PE). Perplexity in word translations exhibits the variation of generated target texts, which is always higher in translation from scratch than in postediting.x

is often accepted without changes and all post-editors therefore often accept the same word translations.

Some PoS tags in the Spanish translation exhibit more variation than the German translation. For example, there is less variation in the translation of superlatives (JJS) in German while there is a relatively large amount of variation in the Spanish translation. Other word classes (e.g. conjunctions) seem to be translated in the same way by most translators and post-editors. The difference between post-edited texts and translations from scratch, however, are more pronounced for Spanish than for German. This suggests that Spanish post-editors accept MT output more frequently than German post-editors.

### **4.4 Translation revision**

According to Gutt (1991) the aim of a translation is to achieve appropriate contextual effects in the target language without unnecessary effort for the reader of the target text, so that the translation corresponds to the original source text in terms of relevant aspects. In order to achieve this goal, translators consciously keep track of the possible associations between stimulus, context and interpretation, so that the resulting translations obey to the principle of cognitive and communicative relevance (Sperber & Wilson 1995: 260).

Translation revision is in many cases a compulsory activity to generate intelligible and optimal relevant translations. A distinction is made between otherrevision and self-revision. Other revision is carried out by someone other than the translator, while self-revision (or checking) is done by the translator him- or herself. Self-revision of a translation is an integral part in the translators' translation process. Jakobsen (2003) distinguishes between online revision, i.e. revision during the translation drafting process, and 'end revision', which occurs after the completion of the first draft without delay. According to Mossop (2007: 109), revision may be defined as "that function of professional translators in which they identify features of the draft translation that fall short of what is acceptable and make appropriate corrections and improvements". Revisions may be due to problems in transfer, content, language and presentation (Mossop 2007) and may take place in translators' minds during the decision-making process ('internal revision') or appear on paper or the computer screen when actual changes are being made ('external revision', Künzli 2007).

Relevance Theory considers words and phrases to encode procedural components that contain instructions which control procedures that limit calculations of conceptual representations. This distinction is known as conceptual and procedural encoding. Procedural encoding thus guides the conceptual computations

and leads to processes of comprehension so that the reader may work with a conceptual representation (Blakemore 2002). In accordance with these observations, Sperber & Wilson (1993: 16) note that "conceptual representations can be brought to consciousness; procedures cannot. We have direct access neither to grammatical computations nor to the inferential computations used in comprehension."

However, according to Alves (2003), translators consciously learn how to manipulate conceptually and procedurally encoded information. They suspect that conceptually encoded information is easier to translate than procedurally encoded information as conceptual encoding exhibits a "relatively stronger interpretive resemblance between source and target texts" (p 20). Sekino (2012) reports findings based on translation data for Japanese into Portuguese. Their results corroborate Alves (2003), showing that processing effort is greater when dealing with procedural encodings in both from-scratch translations and in postediting tasks in terms of keystrokes, fixation counts and fixation duration.

In order to assess these findings with our data, we investigate ST reading patterns and TT revision patterns on a set of UAD which included that shown in Table 4. The duration of the fixations – and also of the first fixation – signals the cognitive effort for processing a word. Fixations tend to be longer on words that require effortful processing as, for instance less frequent words, words containing spelling errors, ambiguous words, words which are inappropriate in a given context, etc. McConkie & Yang (e.g. 2003: 413).

We adopt Alves & Couto-Vale (2011) notion of micro units to quantify the amount of self-revision. A micro unit (*Munit*) is a typing burst which contributes to the translation of an ST token and which does not contain inter-keystroke pauses of more than 1 second. An *Munit* — in the way we use it here — indicates how the translation of a source word was modified. The number of *Munits* that the translation of a word is involved in is thus an indicator for its translation effort, since each revolving modification is an indicator for restructuring or reconsidering the translation a larger context.

We PoS-tagged the English source texts8 and investigated their translations into Danish, German, Spanish, Estonian, Chinese, and Hindi (i.e. the studies ACS08, BD08, BD13, BML12, HLR13, KTHJ08,MS12, NJ12 and SG12 (cf. Carl et al. 2016)) with the hypothesis that:

1. procedurally encoded words in the English source texts would require relatively more reading time

8we used the Penn treebank PoS tagset https://www.ling.upenn.edu/courses/Fall\_2003/ling001/ penn\_treebank\_pos.html

2. their translations into the target languages would require more revision time than conceptually encoded words

To this end, we classified the English PoS tags into 2 bins, labeled *conceptually encoding* and *procedural encodings*, according to the list shown in Table 5. We assume that the word classes in the two bins are more likely to encode their respective labels than the other one. We then investigated the distribution of effort according to the two hypothesis for these two classes.

Table 5: Penn treebank PoS tags for English source texts

The data for the dependent variable total reading time of the ST token (*TrtS*) was analyzed in the same way as the data for the dependent variable *Dur* described in §4.3. The dependent variable *TrtS* was log transformed because it was not normally distributed. Data points which were 2.5 standard deviations below or above a participant's mean were excluded (< 3%).

The final LMEMs had the following random variables: item, participant, text and study. The predictors were *Prob1* (ST frequency), *LenS* (word length in characters), *STseg* (sequential position of sentences in the ST), *Encoding* (see Table 5), *HTra* and *Cross*. These latter two variables implement the literality metric introduced above:

Table 6 and Figure 10 show that translators are likely to spend more time reading conceptually encoded source text words than procedurally encoded ones. The Table shows

Table 6: Effects of *Prob1*, *LenS*, *STseg*, *HTra*, *Cross* and kind of encoding on on total reading times of source text words.

Figure 10: Effects of *Prob1*, *LenS*, *STseg*, *HTra* and *Cross* on total reading times of ST words

Word frequency, word length, word translation entropy and relative translation distortion (i.e. *Cross*) have all a highly significant positive effect on total reading time of ST words. These findings are not surprising and have been reported elsewhere (e.g. Schaeffer & Carl 2014). Also, the facilitation effect for later (higher number) segments in the source text is well known (Schaeffer, Dragsted, et al. 2016).

The picture is different as it comes to translation revision. Table 7 and Figure 11 show that translators revise translations of procedurally encoded words more often than translations of conceptually encoded words. The dependent variable (*Munit*) indicates how often a translator revises a translation.

The analysis for the dependant variable *Munit* was carried out in the same way as previous analyses, but it was not log transformed. Data points which were 2.5 standard deviation below or above a participant's mean were excluded (< 4%). The model included the same random variables and predictors as previous analyses, with the difference that the length (in characters) of the TT word was chosen, given that this might have a more direct effect on revision than the length of the ST word. Similar to the total reading time on the ST in Figure 10, the length of the translated word, the word translation entropy and relative translation distortion (i.e. *Cross*) have all a highly significant positive effect on the number of revisions (*Munit*). This is in line with the findings that are discussed in the context of Figure 8 which show a strong effect of observed translation variants on production time.

The results of this study suggest that there is an asymmetry in the perception and in the production of conceptually and procedurally encoded information in translation. While the perception of procedurally encoded information seems to be less effortful than that of conceptually encoded information, our findings indicate the reverse relation for translation production. Taking the number of revisions as an indicator for the effort in translation production, our dataset shows that the generation of translations for procedurally encoded information is more difficult than that of that of conceptually heavy words.

### **5 Conclusion**

Translation is an extremely challenging task that requires a translator to possess unique skills. Aside from bridging linguistic divergences between both languages, such as e.g. syntactic shifts and lexical decisions, translators must also align the author's intention with the readers' expectations while simultaneously ensuring socio-cultural interpretations of the original text in the translation. The

Table 7: Effects of *LenT*, *STseg*, *HTra* and *Cross* on translation revision

Figure 11: Effects of *LenT*, *STseg*, *HTra*, *Cross* and *Encoding* on translation revision

foundation of this activity seems to be based on unconscious memory processes: the implicit memory of source text segments primes the translator to produce a translation which is structurally and lexically similar to the target text. Subliminal priming mechanisms are the basis from which translations emerge. The first fixation is a very early behavioral measure and the word translation entropy (*HTra*) and relative translation distortion (*Cross*) have an effect on its duration such that words with small *HTra* and/or *Cross* values are easier to process than words with high *HTra* and *Cross* values.

On top of the early priming processes, translators develop a number of more consciously accessible translation strategies providing criteria to decide whether the generated translations conform to his or her expectations. This meta-linguistic knowledge is instrumental for problem-solving during the translation process. The deployment of meta-linguistic knowledge, for instance about grammatical structures or lexical translation equivalence, can be consciously directed and manipulated. For instance, repeated re-reading of a word or phrase is evidence of conscious processes. However, these processes are difficult to disentangle based on typical fixation measures such as total reading times. Each fixation on a word adds to its total reading times bt it is difficult to know which meta-linguistic strategies and problem-solving activities have been used.

Some independent variables, such as word translation entropy (*HTra*) and relative translation distortion (*Cross*) have an effect on both early and late processes, which seem to suggest that early automatized processes trigger certain later conscious ones (Schaeffer & Carl 2013). The results presented by Schaeffer, Dragsted, et al. (2016)suggest that target language-specific aspects play a role right from the beginning in the translation when reading a source text word for the first time. Words with fewer alternative translations and which do not require re-ordering in the target language require less effort than words with a higher number of alternative translations and which must be syntactically re-ordered — and this effect can be observed in early and in late measures.

The lack of appropriate late (eye movement) measures makes it difficult to assess in detail which translation strategies were deployed: a total reading time of 8 seconds, for instance, is just a conglomerate of fixation durations, but it does not tell us which translation processes were used during these 8 seconds.

The analysis of behavioral pattens is much better suited to assess translation strategies. Think Aloud Protocols (Krings 1986; Lörscher 1991; Jääskeläinen & Tirkkonen-Condit 1991) provide evidences for the existence of different translation strategies. However, the analysis of TAP data is very labor intensive and it is unclear how the identified translation strategies relate to the UAD. Alternatively,

behavioral patterns can be segmented and identified in the UAD to investigate translation strategies. Schaeffer, Carl, et al. (2016), for instance, show that translation processes are much less sequential, (sentence-by-sentence, chunk-by-chunk) and much less stratificational than predicted by earlier translation models.

To date, empirical translation process research has mainly focused on the textual product-based angle and there are some insights as to which linguistic constructions are more or less difficult to translate. However, besides some work into keystroke pause analysis (Immonen 2006; Lacruz et al. 2012), very little work is available that investigates in detail the temporal structure of the translation process and that systematically relates translation strategies to observable behavioral patterns.

In this chapter we develop a computational noisy channel model of the translation process, which can take into account a (possibly large) number of probabilistic functions that contribute to and explain the translation process. Prerequisites for the modelling of the process are measures and metrics that quantify different aspects of the observed data and that describe the various different early and late hidden translation processes in a translator's mind.

While translation process research investigates the underlying factors that lead to successive intermediate versions of a text that is to become a translation, corpus-based translation studies, including translation universal research, investigates regularities in different (final) translations, however, usually without access to the directly observable translation behavior. There are thus a number of similarities in translation product and translation process research, as both investigate the regularities in different (versions of) translations and the underlying mechanisms which may explain the observed regularities.

With the elaboration of a noisy translation process model, we hope to achieve "a scientifically sounder methodology of data collection, analysis and report" which will help in "the development of a relatively uncontroversial classification of process indicators" (Bernardini 2001: 260).

### **References**

Alabau, Vicent, Ragnar Bonk, Christian Buck, Michael Carl, Francisco Casacuberta, Mercedes Garcia Martinez, Jesús González, Philipp Koehn, Luis Leiva, Bartolomé Mesa-Lao, Daniel Ortiz, Herve Saint-Amand, Germán Sanchis & Chara Tsoukala. 2013. CASMACAT: An open source workbench for advanced computer aided translation. *Prague Bulletin of Mathematical Linguistics* 100(1). 101–112.

(eds.), *Proceedings of symposium on eye tracking research and applications*, 299– 302. New York: Association for Computing Machinery.

## **Chapter 5**

## **Language processing and translation**

### Moritz Schaeffer

Johannes Gutenberg University of Mainz

### Michael Carl

Renmin University of China

The current chapter reviews studies which investigate the behavioural differences during reading and writing for translation and other non-translational language use. This chapter further argues that eye movement measures imported from Psychology are not well suited to describe the unique co-occurrence of reading and writing during written translation. In order to address these shortcomings, one existing measure (the Eye-Key Span, Dragsted & Hansen 2008; Dragsted 2010), which describes how reading and writing activities are coordinated, is further tested by replicating existing findings with more language combinations and participants. A second, novel measure (the probability that source text reading and target text writing overlap in time) is used in conjunction with the Eye-Key Span to test predictions from an existing model of the translation process (Schaeffer & Carl 2013a). Finally, one new feature (HCross) is introduced with which an existing model of bilingual memory (Hartsuiker et al. 2004) is extended.

### **1 Translation and non-translational language processing**

There is a long tradition of studying the differences between original texts written in one language and texts translated from a different language – in terms of the product of translation, i.e., in corpora of the final (published) texts (e.g. Hansen-Schirra et al. 2012). Corpus-based translation studies have the great advantage that the data which led to the formulation of theoretical insights is ecologically valid to a high degree: the texts used in corpora such as the CroCo corpus (Hansen-Schirra et al. 2012) are published texts and have therefore been produced in situations which are real and natural. Experimental studies, on the

Moritz Schaeffer & Michael Carl. Language processing and translation. In Silvia Hansen-Schirra, Oliver Czulo & Sascha Hofmann (eds.), *Empirical modelling of translation and interpreting*, 117–154. Berlin: Language Science Press. DOI:10.5281/zenodo.1090958

### Moritz Schaeffer & Michael Carl

other hand, often manipulate source texts (henceforth ST) and the STs are normally far shorter than those in real life situations (ranging from single words, to single sentences and short texts of approximately 150 words). In addition to the unnatural characteristics of the STs, participants are often not allowed to use reference material such as dictionaries or glossaries and typically do not have access to the internet. Further increasing the unnatural conditions of experimental studies is the fact that participants translate knowing that their reaction times or keystrokes and/or eye movements are recorded and the simple presence of a researcher may further impinge on the process of translation. However, the shortcoming of corpus-based translation studies is that it is difficult to attribute observed effects to particular aspects of the translation *process*, given that the source of information is typically the frequency of a particular item in the *final product*. The factors which led to the observed result of the process remain hidden in the dialogue between ST reading and target text (henceforth TT) reading and writing and interaction with other information sources.

The current study therefore aims to provide insights into the cognitive processes which occur during translation by first reviewing existing studies which compare translational and non-translational language use and by comparing the effect of two tasks (monolingual copying and translation) on two behavioural measures. One of these behavioural measures was first proposed by Dragsted & Hansen (2008) and Dragsted (2010), and the second behavioural measure is novel. The two measures take into account both eye movements on the source text and typing activity. The eye-key span (Dragsted & Hansen 2008; Dragsted 2010) describes the temporal distance between a first reading of a particular word and the first keystroke which contributed to the translation of that particular ST word. It can be seen as a relatively late indicator: Many intervening processes between a first reading and the first keystroke can and typically do occur during translation, while fewer occur during monolingual copying. The second measure is the probability that ST reading and TT typing occur (at least partially) at the same time. It is an indicator of cognitive effort: the less likely the co-occurrence of these two processes, the more effortful the process. The more likely it is that reading and writing overlap in time, the less effortful is the process as a whole at that time. These two measures take into account one aspect of the nature of the translation process which it shares with few other tasks, apart from monolingual copying: the direct relationship between read input and written output.

Jakobsen argued that with the introduction of eye tracking and keylogging into translation process research the hope was that

…eye data would provide evidence pertaining identifiably to source-text reading so that source-text comprehension processes could be studied sep5 Language processing and translation

arately from text-production processes and could be compared with other reading processes that were not part of a translation process. (Jakobsen 2011: 41)

Very few studies have systematically compared the cognitive processes during non-translational language use with those that occur during translation. The current chapter will review the studies which have done so and will provide new evidence which addresses shortcomings in existing studies.

### **2 Reaction times and eye movements during translation**

### **2.1 Reaction times per clause**

Shreve et al. (1993) compared reading times in three tasks and groups: reading for later translation by translation students, reading for later monolingual paraphrasing by students of English and reading for comprehension by students in psychology. Reading times were measured per clause (including re-reading) and normalised by the number of words in each clause. Results from principal component analysis of the reading times showed that, at least on the basis of these behavioural measures, none of the four factors of the principal component analysis distinguished reading for translation clearly from the other two tasks. However, reading for translation was overall more similar to reading for monolingual paraphrasing than to reading for comprehension. The authors further point out that there was more variation in how translators read for translation while the other two groups of participants approached their tasks more homogeneously. The paraphrasing and translation groups were also asked to indicate post-task the nature and number of problems in the clauses they identified in their reading. The expectation was that the number of problems identified post-task would correlate with reading times. This was not the case. Although the authors do not interpret their findings in this way, it is entirely possible to argue that post-task identification of problems might not accurately reflect the processes which occurred during reading, given that they are produced off-line. One other reason might be the fact that reading times per clause might not accurately reflect actual reading times, which might show the expected effect locally rather than globally.

### **2.2 Reaction times per word**

In a series of studies, Bajo and colleagues (Macizo & Bajo 2004; 2006; Ruiz et al. 2008) employed more sensitive behavioural measures, i.e., reaction times per

### Moritz Schaeffer & Michael Carl

word using the self-paced reading paradigm. In all three studies, a similar experimental design was used: masked self-paced reading is the sequential presentation of single words which is controlled via button press by the participant, so that subsequent button presses are used to measure reaction times per word. The interval between two successive button presses is taken as an indication of the time needed to process the currently displayed word. These studies therefore address the concerns raised in relation to the study by Shreve et al. (1993). Bajo and colleagues (Macizo & Bajo 2004; 2006; Ruiz et al. 2008) refer to the model proposed by Seleskovitch (1976) who argued that translation is normally carried out sequentially in that the first step is source text comprehension and only when this is complete and only once the source material is "deverbalised" can reformulation in the target language begin. Opposed to this sequential view is the assumption that representations specific to the target language (TL) are activated at the same time as source language (SL) representations are activated (horizontally and in parallel). The vertical model by Seleskovitch (1976) is essentially what in machine translation would be called an interlingual model. It is the highest level in the Vauquois triangle (Vauquois 1968) (see Figure 1), where transfer occurs at a language-independent interlingual representation, common to all languages.

Figure 1: The Vauquois triangle of translation based on Vauquois (1968)

The studies by Bajo and colleagues were designed to test the Seleskovitch model. Participants in all three studies carried out two tasks: reading for com-

### 5 Language processing and translation

prehension and reading for translation. Participants were not overtly producing the translation while reading – they were asked to orally produce the translation after having read the sentence (for translation). The expectation in all three studies was that the manipulation of the stimuli would elicit an effect only in the reading for translation condition, because of a) increased working memory load due to the added effort related to online translation and b) because the assumption was that during reading for comprehension the TL would not be activated and TL-specific manipulations would not have an effect on source text (ST) reading. In the 2004 study, Macizo and Bajo manipulated both working memory load and the availability of pragmatic cues. The stimuli consisted of object relative sentences such as "The judge that the reporter interviewed dismissed the charge at the end of the hearing." The authors argued that working memory load would be particularly high for the verbs of the main clause and the relative clauses, because in object relative clauses, the thematic roles of the first two constituents (*judge* and *reporter* in the example) can only be assigned retrospectively once the subordinate verb (*interviewed*) is read. Pragmatic cues consisted of verbs which were either more or less predictable based on the previous context. It is, for example, more predictable that a reporter interviews than that a reporter admires or it is more predictable that a judge dismisses a charge than that he drives a car.

In addition to testing the sequential versus parallel view of translation, Macizo & Bajo (2004) tested the predictions of the Revised Hierarchical Model (RHM) of bilingual memory (Kroll & Stewart 1994) which predicts that backward translation (BT, from L2 into L1) is faster than forward translation (FT, from L1 into L2), because L2 lexical representations have stronger connections to their L1 equivalents than to shared conceptual representations. Translation from L2 into L1 is therefore predicted to use the faster lexical routes and translation from L1 into L2 is mediated by the less direct conceptual connections. However, during translation, both routes are always activated – one is simply faster than the other. The predictions based on the sequential/parallel model and the RHM are therefore that an effect appears only in the reading for translation condition and that FT, because it is more conceptually mediated than BT, is especially susceptible to the manipulation of pragmatic cues. These results are clearly borne out by the evidence: Reaction times were significantly slower during reading for translation, particularly during FT and particularly for the constructions which require retrospective assignment of thematic roles and therefore high working memory load, supporting the parallel activation of SL and TL representations during reading for translation. In addition, more predictable verbs were read significantly faster than less predictable verbs in FT, but not in BT, supporting the predictions of the RHM.

### Moritz Schaeffer & Michael Carl

Further support for the co-activation of SL and TL representations during reading for translation was provided by the two subsequent studies (Macizo & Bajo 2006; Ruiz et al. 2008). In both studies, participants also read single sentences for comprehension and for translation in a self-paced reading paradigm. In the 2006 study, the stimuli for experiments 1a and b consisted of interlingual homographs which created an ambiguity only if they were translated: the Spanish word *presente* is not ambiguous in Spanish (it can only refer to the present time), but it is ambiguous when translated into English, given that *present* can refer both to a gift and the present time. In experiment 1a and b, the number of words intervening between the ambiguous homograph and the disambiguating context was manipulated so that working memory load was a factor in the design. In experiment 2a and b, cognates were used. The manipulation in experiments 1a and b was expected to result in inhibition only when the reading purpose was translation and particularly when the working memory load was high, but not when the reading purpose was comprehension alone. The prediction for experiment 2a and b was that the presence of cognates would facilitate. Both of these predictions were designed to lend further support to the hypothesis that activation of the TL during ST reading is task-dependent. Again, the predictions were confirmed in this study. The 2004 study by Macizo and Bajo only employed professional translators, but the 2006 study by the same authors replicated the effects found in professional translators with innocent bilinguals who had no professional translation experience: interlingual homographs, the working memory manipulation and cognates resulted in the same pattern of results, suggesting that the mechanisms underlying the task-dependent co-activation of SL and TL is not a function of expertise, but co-extensive with bilingualism.

The 2008 study by Ruiz et al., again, employed essentially the same experimental design as the previous two studies. TL-specific aspects were manipulated here: the frequency of critical SL items was kept constant while the frequency of their TL equivalents was either high or low (experiment 1). Experiment 2 manipulated the congruence of the word order in the ST with that in the TT: In the SL Spanish, adjectives can either precede the noun they modify or they can be placed after it while in the TL (English) they can only precede it. Only professional translators participated in this study and working memory load was not manipulated. Results were as predicted, in that the manipulations only had a significant effect on reaction times when the reading purpose was translation, but not when the reading purpose was comprehension only.

All three studies by Bajo and colleagues support the horizontal model of translation. All three studies show that co-activation of SL and TL is task-dependent.

### 5 Language processing and translation

In all three studies by Bajo and colleagues, the results are interpreted in terms of Grosjean's (1997) language mode continuum, which predicts that, depending on the context of language use, a bilingual's two languages are activated to varying degrees. At one extreme is the monolingual mode, in which mainly one language is active and at the other extreme is the bilingual mode in which both languages are active.

### **2.3 Complete texts and eye movements**

Jakobsen & Hvelplund Jensen (2008) investigated essentially the same question as all the studies presented thus far, but employed an eye tracker. In this study, there were four tasks: reading for comprehension, reading for translation, reading while speaking a translation and reading while writing a translation. The expectation was that the task would have an effect on eye movements. The authors found significantly more fixations on the whole ST in reading for later translation than reading for comprehension, reading while speaking a translation had significantly more fixations than reading for translation and reading while typing a translation had significantly more fixations than reading while speaking a translation.

Further support for task-dependent co-activation of two linguistic systems comes from the study by Hvelplund Jensen et al. (2009). The manipulation in this study is very similar to the one by Ruiz et al. (2008), in that it investigates the congruence of word order. In the study by Jensen et al., the stimuli consisted of complete Danish texts which were translated into English. In the critical declarative clauses, embedded in the longer texts, the subject either preceded (SV) or followed the verb (VS). When translating these clauses into English, participants had to invert the order of verb and subject for the VS clauses, but not for the SV clauses. As in the study by Ruiz et al. (2008) the expectation was that it would be more difficult to process the incongruent clauses than the congruent ones. Results confirmed this. Jensen et al. employed an eye tracker and so the dependent variable was total reading time on the phrases. Total reading time is the sum total of all fixations on the area of interest. During translation, participants (professional translators) looked longer at clauses which had an incongruent word order than at clauses with a congruent word order. The fact that this effect is task dependent came from a follow-up study (Winther Balling et al. 2014) which employed the same stimuli as in the previous study, but in this case, the participants were either Danish-English bilinguals or English-Danish bilinguals and they were asked to read for comprehension only. The participants were therefore asked to read in their L1 and L2 respectively. The rationale for the follow-up

### Moritz Schaeffer & Michael Carl

study was to make sure that the effect observed in the 2009 study was in fact taskdependent and not due to the fact that VS clauses are inherently more difficult to process when reading for comprehension in either L1 or L2. The manipulation (VS vs. SV) had no effect on total reading time during reading for comprehension in either L1 or L2.

One question, which is relevant in this context is how early the effect of the coactivation of the two linguistic systems during translation appears. The study by Shreve et al. (1993) employed a very late measure (reading latency of a complete clause), the studies by Bajo and colleagues employed a more sensitive measure (reaction time per word). The studies by Balling and colleagues ((2009; 2014)) employed total reading time on a phrase. Total reading time, given that it is the sum total of fixations on a particular region of text, is not informative regarding the time course of the effect.

### **2.4 Early and late eye-movement measures**

Schaeffer et al. (2017) employed more fine-grained eye movement measures than previous studies, but otherwise, the design was similar to previous research. Professional translators read for comprehension and translated single sentences. The manipulation consisted of the number of target words which were equivalent to a single source word. Half of the stimuli contained items which had a one-toone equivalence (the likelihood that an ST word was translated using just one TT word was high) and the other half contained one-to-many equivalences (the likelihood that an ST word was translated into more than one TT word was high). Global analyses showed that average fixation durations were 20ms longer during reading for translation than during reading for comprehension. Participants made on average 16 fixations more per sentence during reading for translation and the number of regressions also doubled, as did total reading time. The significant increase in all these eye-movement measures confirms and extends findings from earlier studies discussed above, i.e., that during reading for translation, coactivation of the two linguistic systems increases effort from early on (duration of single fixations) and into later processes (total reading time and regressions). That co-activation occurs very early during the process is further supported by the fact that the manipulation had a significant effect on first fixation durations: when it was likely that an ST word would be translated using more than one word, participants spent 23ms longer on this word when they were to translate it afterwards, but not when they only had to read it for comprehension.  The critical items which were likely to be translated using more

### 5 Language processing and translation

than one TT word necessarily introduced lexical items which, when translated back into the SL, had no direct equivalents (see examples 1a and 1b below) in the context in which they appeared. It is therefore likely that, in the context in which they appeared, the one-to-many items did not share semantic representations across the two languages to the same degree as did the one-to-one items. First fixation durations on one-to-one items were not significantly different from first fixation durations on either kind of item during reading for comprehension. This pattern of results suggests that if the overlap in terms of lexico-semantic representations between SL and TL items is high, as in the case of one-to-one items, then translators are able to exploit the effects of co-activation and (initial) processing is similar during reading for comprehension and reading for translation. If, however, the semantic overlap is smaller, as in the case of one-to-many items, co-activation has an inhibiting effect on reading for translation, but not on reading for comprehension.

(1) a. One-to-many

'The water in the bottle is *low*…' In In the der bottle Flasche is ist *not nicht any more mehr much viel* water… Wasser…

b. One-to-one

'The water in the bottle is *bad*…' The Das water Wasser in in the der bottle Flasche is ist *bad*… *schlecht*…

Further support for the early activation of TL-specific representations during ST reading comes from a corpus-based eye movement study (Schaeffer et al. 2016). This study was designed to test a model proposed by Schaeffer & Carl (2013b). While the studies by Bajo and colleagues and Balling et al. described above contrasted a sequential and parallel model of translation, the model by Schaeffer and Carl argued that translation is best represented by both early, parallel and late, sequential processes. Schaeffer and Carl hypothesised that early automatic priming processes activate semantic and syntactic representations which are shared by the SL and the TL and later, more conscious, essentially monolingual vertical processes monitor the output from the early processes. Shared syntactic representations are defined in terms of the shared syntax account (Hartsuiker et al. 2004) and shared semantic representations are defined in terms of the Distributed Feature Model (de Groot 1992). In line with these models, Schaeffer and Carl argue that "shared representations are accessed very early during the process" (Schaeffer & Carl 2013b: 174) and that during the early stages "there is no

### Moritz Schaeffer & Michael Carl

conscious control over how source and target are aligned cognitively" (Schaeffer & Carl 2013b: 173). In order to test the possibility that the automatic cognitive alignment has an observable effect on early eye movement measures and that these primed, shared representations serve as a basis for later processes, Schaeffer et al. (2016: 189) quantify the syntactic similarity (in terms of word order) of the source and the target texts and the variation of word translation realizations. The metric termed Cross (Carl et al. 2016: 26) describes the relative word order differences between the ST and the TT. If the word order is identical in two segments, then the Cross value for each word is 1. If, say, the equivalent of the first ST word is aligned to the sixth TT word, then the Cross value is 6. If, however, the distortion is in the opposite direction, i.e., if the sixth TT word is aligned to the first ST word, then the Cross value is -5. The Cross value can be computed by counting how many TT words need to be progressively or regressively counted in order to arrive at the equivalent of a given ST word. It is then termed CrossS. But the Cross value can also be computed by counting the number of ST words which need to be read progressively or regressively in order to arrive at the equivalent of a given TT word. This is then termed CrossT. CrossS can be seen as a process by which the ST is cognitively aligned with the TT, while CrossT describes a process which aligns the TT with the ST. The computation of CrossS progresses in a linear and sequential manner through the ST and finds aligned TT items, while CrossT progresses in a linear and sequential manner through the TT and finds aligned ST items.

The variation in terms of TT realizations of a particular ST item is computed by counting how many different TT items, which are aligned to the same ST item, there are in a corpus of a number of translations of the same ST. On the basis of the probabilities of each of these TT realizations, the distribution of these probabilities is then calculated. This is then expressed as word translation entropy (HTra) (Carl et al. 2016: 31) if the variation underlying this metric is lexical in nature, and it is termed syntactic entropy (Bangalore et al. 2016) if the underlying variation is syntactic in nature.

Schaeffer et al. (2016) find that both word translation entropy (HTra) and syntactic distortion (CrossS) have a significant positive effect on first fixation durations and total reading time. It is therefore likely that the effect of CrossS and HTra on first fixation durations represents early, automatic cognitive alignment, which is less effortful in the case of ST items for which the overlap between ST and TT representations in terms of syntax and lexico-semantics, respectively, is greater (low HTra and Cross values). The study by Bangalore et al. (2016) found that syntactic entropy had a significant positive effect on total reading time of

### 5 Language processing and translation

source text segments. The studies by Bangalore et al. (2016) and Schaeffer et al. (2016) found evidence of the above in the TPR-DB (Carl et al. 2016), which is a large database containing eye-movement and keylogging data in relation to several translations of the same source texts into a large number of target languages. The data for the study by Schaeffer et al. (2016) consisted of 42,211 English ST words translated into six different target languages and the data for the study by Bangalore et al. (2016) consisted of 26,139 words translated from English into three different target languages. While the large number of languages and the sizeable amount of material warrants confidence in the results, it should be stressed that a non-negligible amount of variation could not be explained with the predictors in the model presented by Schaeffer et al. (2016). In other words, while the model could make predictions with a certain degree of confidence, a possibly large number of variables which impact eye movements during translation remains unknown. To sum up, it is likely that task-dependent co-activation occurs early (horizontally) and that later processes use the output from these relatively automatic processes in the relatively vertical processes. The time needed to process a particular ST item is likely to be a function of the degree of overlap between ST and TT syntax and/or semantics.

### **3 Automatic translation**

The studies reviewed so far have found that co-activation during translation is task-dependent. However, there is evidence which suggests that activation of translation equivalents is automatic even if participants are explicitly asked to ignore verbal stimuli (Wu & Thierry 2012; Wu et al. 2013). In the 2012 study by Wu and Thierry, participants were asked to perform a go/no-go task in which they had to respond with a button press to the presentation of shapes (circles or squares) while electrophysiological data were recorded. Half of the trials consisted of words. Participants were told to ignore the words and only respond to the shapes. Unbeknown to the participants, 30% of the word trials consisted of English words, which, when translated into Chinese, were homophone with the Chinese words for circle or square. Behavioural responses to the critical items showed that Chinese-English bilinguals were not likely to make more erroneous responses to the critical items (English words which when translated into Chinese sounded like either circle or square) than to control items (English words which were unrelated to the Chinese sounds for circle and square). However, ERP results (results from the recorded electrophysiological data) showed that the manipulation resulted in an N200 effect. The N200 effect is normally observed in

### Moritz Schaeffer & Michael Carl

situations in which conflicts of a linguistic or non-linguistic nature are the underlying cause. What the study by Wu and Thierry thus shows is that, although the Chinese-English bilinguals were told to ignore all the word trials and only respond to shapes, Chinese translations of the English words were nevertheless activated automatically and early (200-300ms). The fact that this did not translate into a motor response and increased erroneous responses to critical word trials shows that the Chinese-English bilinguals were not necessarily aware of the co-activation and/or inhibited the Chinese equivalents. This interpretation is in line with the Inhibitory Control (IC) model (Green 2003) which predicts that the non-target language, i.e., the language which is not intended to be used in a given task, is inhibited to varying degrees. The 2013 study by Wu et al. showed very similar effects in an eye movement study.

It is therefore reasonable to think that the failure to find a co-activation effect during reading for comprehension in the studies by Bajo and colleagues and Balling et al. is due to the fact that the behavioural dependent variables are not sensitive enough to detect (inhibited) co-activation during reading for comprehension.

### **3.1 Independent translation routes**

García (2015) reviews 21 cases of pathologies in bilinguals who presented with disorders which affected their translation behaviour. Though limited, this evidence makes exciting neurofunctional predictions regarding the relationship between languages in bilinguals. The most interesting of these hypotheses is that "Lexical translation routes are independent from those supporting monolingual production" (García 2015: 131). In other words, the suggestion is that there are connections or networks which are exclusively used for translation and not for monolingual language use. The evidence regarding this hypothesis comes from patients who were e.g. unable to spontaneously use one of their languages, but were able to translate from or into it. If it is confirmed that some form of translation route is independent of monolingual language use, this would explain how translators and interpreters are able to navigate the competing demands of a linguistic system which is essentially non-selective and which inhibits the SL to some degree, while still allowing it to be used for reading or listening and while activating the TL only rather than also the SL for production. This argument must remain speculative, given current evidence, but, should it find further support, it is entirely possible to argue that the unique and repeated exposure to translation or interpreting tasks may strengthen and possibly expand the nature of these translation routes which are independent of monolingual language use

### 5 Language processing and translation

and which are co-extensive with bilingualism. It is further possible to hypothesise that these routes are likely faster than those routes which are also active during monolingual language use, because they do not face the competing demands emerging from an essentially non-selective system which needs to inhibit the non-intended language system. In addition to the speed and strength of these translation routes, a third hypothesis may be articulated: it is possible that lexical items which are translated very frequently in the same way (low HTra) may result in better established translation routes than items which are translated in different ways when encountered in context. In other words, the strength and availability of these routes may be a function of their semantic overlap.

So far, only reception-related processes have been considered, but, as will be shown in the remaining sections, translation also has an effect on typing behaviour.

### **4 Monolingual text production and translation**

Very few studies have systematically studied the difference between monolingual writing and typing during translation – in terms of the cognitive process and on the basis of behavioural data (as mentioned above, corpus based translation studies have investigated the differences between original and translated texts successfully and extensively). The studies by Immonen (Immonen & Mäkisalo 2010; Immonen 2006; 2011) are a notable exception. Immonen (2006) had 18 Finnish professional translators carry out two tasks: the author asked participants to write a short original text in their L1 (Finnish). The second task consisted of a translation of a text from English (L2) into Finnish (L1). Immonen asked participants to write an informative presentation based on a brochure which was a guide for those planning a career in the European Commission. The ST for the translation task was similar in register and topic – it was a text about the unity of the EU and had been used in exams for translators applying for a post at the EU. No particular brief was given for the translation task apart from the requirement that they should have publishable quality. Both tasks were recorded with the keylogging software Translog (Jakobsen & Schou 1999). One obvious difference between writing an original text and translation was that, at least on the basis of the raw means, participants spent proportionally more time drafting during original production (73%) than during translation (63%). Participants also spent less time revising after writing the original text (11%) than after drafting the translation was finished (24%). Immonen (2006: 323) classified all pauses according to where on the linguistic hierarchy they occurred: preceding a paragraph,

### Moritz Schaeffer & Michael Carl

a sentence, within a clause, preceding a word, a compound boundary within a word, preceding a syllable within a word and within a word other than at the compound or syllable boundary. Of course, a pause preceding a paragraph is also a pause preceding a word and a sentence, but Immonen defined a pause always at the highest possible level of the hierarchy of linguistic categories. So a pause at the beginning of a paragraph is a pause preceding a paragraph (the highest rank), not a sentence or a word. Immonen found that the distribution of pause lengths was similar in original writing and translation in that the higher up in the linguistic hierarchy the pauses occurred, the longer they were in both tasks. However, pauses within a word (both at the syllable boundary and elsewhere word medially) were significantly longer during translation than during original text production. Pauses between words were also significantly longer during translation than during original text production. However, at the sentence and paragraph boundaries, pauses during original text production were significantly longer than during translation. Immonen (2006: 333) argues that macro-level planning may be the driving force behind the longer pauses during original text production at the higher levels of the linguistic hierarchy, given that pauses between paragraphs and sentences are mainly used for this kind of planning. During translation, macro-level planning may be less important. Decisions between a number of possible lexical items and between different word orders or other syntactic choices may be more effortful during translation than during original text production and hence lead to longer pauses at the lower levels of the linguistic hierarchy, where these choices are relevant.

28 professional translators participated in the study by Immonen (2011). Participants carried out the same tasks as in the previous study. A very similar pause classification as that in the previous study was used. In the 2011 study, Immonen defines a processing unit by comparing the pause lengths at the different levels of the linguistic hierarchy for each participant. If the pause lengths to adjacent levels of the linguistic hierarchy did not significantly differ from each other, then they were grouped together. Results showed that grouped processing units were very different in the two tasks. Immonen (2011: 243) thus concludes that "processing units in translation cannot be predicted from the profile in monolingual text production". Immonen clusters the different linguistic levels into three further groups according to what kind of processing takes place: textual (paragraphs and sentences), lexical and syntactic (clauses, phrases and words) and word medial processes. In terms of textual processing, monolingual processing and translation were not significantly different. The most interesting difference between the two tasks was in terms of syntactic processing: on the basis of the clustering,

### 5 Language processing and translation

Immonen (2011) concludes that in monolingual text production, "the weight of syntactic level processing is carried by clauses and words" (244) while in translation, "the emphasis of syntactic processing is on phrases and words." (245) At the textual level, processing clusters were more varied during monolingual text production than during translation, while in the syntactic clusters, the opposite was the case. Immonen therefore suggests that control over processes is stronger at lower levels during translation and that processing occurs in smaller units during translation.

The studies by Immonen compared writing of an original text with translation. However, copying may be a better comparison, given that a copyist, like a translator, has no control over the content of the text that is being produced.

Carl & Dragsted (2012) show, on the basis of an implemented model, that copying can be very similar to translation. Carl and Dragsted show that the model by John (1996) predicts the time a copyist needs to produce a segment with an error rate of less than 5% when the segment is easy to comprehend. However, John's model does not predict extensive re-reading, while the examples Carl and Dragsted provide show that copyists do present such behaviour when the segment is difficult to comprehend. Translation by professionals of easy segments can also be predicted with an error rate of less than 5% by John's model, while translation of segments which are difficult exceed the production time predicted by John's model. In sum, the study by Carl and Dragsted suggests that copying may provide a good contrast to translation because it involves coordination of input and output in a similar manner to how eye movements and typing activity need to be coordinated during translation, so that a difference in the behaviour may be attributed to the involvement of two linguistic systems rather than one. The next sections will show that traditional eye movement measures are not adequate for the description of the extensive re-reading behaviour typical for translation, as observed by the studies discussed so far.

### **5 Beyond the first run**

The dependent variables in eye movement studies during reading typically employed are all based on the assumption that a reader moves from left to right (or from right to left in languages such as Hebrew) in a fairly linear manner. The fundamental criterion for defining dependent variables is what is called a first run. A first run describes a more or less sequential progression through the sentence. A first run is interrupted when a regression to an earlier word is made. All early eye movement measures are defined in relation to the first run: a first fixation

### Moritz Schaeffer & Michael Carl

duration is the time a reader spends on a word before moving on to either an earlier wordn-m, to a later wordn+m, or when the same wordn is refixated. The probability that a word is skipped is also defined on the basis of a first run, i.e., if wordn is fixated, wordn+1 is not while wordn+m is, then wordn+1 is defined as a skipped word, even if it is re-fixated in a later run. The same applies to gaze duration: this measure is the sum of all fixations on a wordn before a wordn+/-m is fixated. Later eye movement measures typically include the spillover duration, i.e., the time spent on (a number of) word(s)n+m, the probability of a regression, the second pass duration and total reading time. Probability of a regression in refers to a situation in which an eye movement is made from a wordn+m to a wordn – so here again, a regression in is defined as a deviation from a linear, more or less sequential progression through the sentence. The second pass duration consists of the sum of fixations which were registered during the second run – if there was one. Total reading time, however, is entirely insensitive to the sequence of eye movement events and simply describes the sum of all fixations on a word irrespectively of when they occurred. The measures described above have also been applied to areas of interest covering several words.

The eye movement measures described above have been very useful for the description of early effects of the text that is being read on how it is processed. However, previous studies (e.g. Jakobsen & Hvelplund Jensen 2008; Schaeffer et al. 2016) have found that reading for translation is especially intense during the later stages of reading. This may have several reasons. On the most basic level, it may have to do with the fact that reading for comprehension is often investigated using single sentences which normally do not form a coherent text: when single sentences are presented one at a time, rereading of earlier text is of course impossible, resulting thus in potentially fewer late eye movement events. During translation, a number of other processes co-occur which may result in more and later eye movement events: typing and the presence of two texts (the ST and the emerging TT). In addition, the ST and the TT are of course in two different languages. During translation, reading occurs typically as a succession of eye movements in the source text followed by eye movements on the target text and shifts from one text to the other are relatively frequent, as is re-reading of already read source and target text (e.g. Jakobsen & Hvelplund Jensen 2008; Hvelplund Jensen 2011). A very rough indication of the importance of late events during reading for translation may be the average total reading time. Kliegl et al. (2004) report a mean total reading time per word during reading for comprehension of 245ms (SD = 48), a subset of the TPR-DB shows that during (monolingual) copying the mean total reading time per word on the source text is 797ms (SD = 1068), however, during translation, the mean total reading time per word on

### 5 Language processing and translation

the source text is 1577ms (SD = 5824). There have been attempts (Hyönä et al. 2003) to develop late eye movement measures which are more adequate for the description of global text processing. However, these eye movement measures, while extending the ones described above, still depart from a first run and, crucially, cannot do justice to the complexities of translation, because they involve one rather than two intimately related texts and these measures do of course not take the relationship of eye movements to typing behaviour into account. The next section will describe an eye movement measure which addresses these shortcomings.

### **6 The eye-key span**

Dragsted (Dragsted & Hansen 2008; Dragsted 2010) developed the eye-key span (EKS) in reference to the ear-voice span which is used to describe the distance between input and output during simultaneous interpreting, typically measured in words or seconds (e.g. Defrancq 2015). While translators do not have the same time pressure as simultaneous interpreters, it is nevertheless the case that translators have to coordinate input and output similarly to copyists and simultaneous interpreters. The eye-key span describes the time that elapses between the first or last time an ST word is fixated before the first key is pressed which contributed to the production of the equivalent TT word(s) (Dragsted 2010: 51). Hansen (2008) found that difficult words result in longer eye-key spans than easy words. The difficulty of the words is described in terms of the number of alternative translations different translators produced for the same source text words. Easy words were translated the same way by all translators and difficult words were translated differently by nearly all translators in the sample. However, only three ST words were analysed and only eight translators participated in the study. Dragsted (2010) also found that professional translators have a shorter eye-key span than student translators. The next sections will present analyses from the TPR-DB, which were designed to replicate and extend the findings from Dragsted (Dragsted & Hansen 2008; Dragsted 2010).

### **6.1 The dependent variable for experiment 1a and 1b**

The EKS was calculated from the first fixation. Only the drafting phase was included, i.e., both orientation and revision were excluded from the analysis. Figure 2 visualises the eye-key span for the ST word "flaring" in the segment "His withdrawal comes in the wake of fighting flaring up again in Darfur…" which has been translated into German.

Moritz Schaeffer & Michael Carl

Figure 2: Progression graph exemplifying the eye-key span from first fixation on the ST word to first keystroke of the equivalent expression.

In Figure 2, the horizontal axis represents time in ms. The left vertical axis represents the ST and the right vertical axis the TT. Blue dots are fixations on the ST, keystrokes are black (insertions) and red (deletions), while fixations on the TT are green diamonds. The first fixation on the ST word "flaring" occurs at around the time of 487,000 during a first, relatively linear reading of the segment. The segment is read again in a far less linear manner before TT production of this segment begins around the time 542,000. The eye-key span (EKS) for this word is therefore roughly 55 seconds. From a first contact with the word, the translator needs to re-read the ST segment twice before they are in a position to produce an equivalent TT item. The aim of experiment 1a was to firstly replicate the findings from Dragsted & Hansen (2008) and Dragsted (2010) in a larger sample involving more participants and target languages. Secondly, the aim was to find factors which can predict the EKS during translation. The aim of experiment 2 was to test how the EKS during copying differs from the EKS during translation.

### **6.2 Experiment 1a: Data, participants and procedure**

For experiment 1, the following studies were used: ACS08, BD08, BD13, BML12, KTHJ08, MS12, NJ12, SG12. The SL for all these studies is English and the TLs are Danish, Spanish, Chinese, Hindi, and German. Together, these constitute 12,474 ST words, 3,242 unique ST items, 108 participants and 12 different STs. The task was always translation.

5 Language processing and translation

### **6.3 Data Analysis**

For all the analyses in the present study, R (R Core Team 2014) and the lme4 (Bates et al. 2014) and languageR (Baayen 2013) packages were used to perform (general) linear mixed-effects models ((G)LMEMs). To test for significance, the R package lmerTest (Kuznetsova et al. 2014) was used, which implements ANOVA for mixed-effects models using the Satterthwaite approximation to estimate degrees of freedom.

Data points which were more than 2.5 standard deviations above or below a participant's mean for the dependent variable were excluded. This resulted in the exclusion of less than 4% of the data. The dependent variable (EKS) was transformed with the natural logarithm because it was not normally distributed.

The final LMEM for the EKS had the following random variables: item, participant, text and target language. The predictors were:

### Moritz Schaeffer & Michael Carl

Collinearity was assessed by inspecting variance inflation factors for the predictors; all values were low (<1.2), indicating that collinearity between predictors was not a problem. Initially, HTra was also in the model and it had a significant positive effect on EKS. However the variance inflation factor was relatively high (1.96) and was therefore excluded from the final model.

Table 1 lists the effects of the predictor variables on EKS and Figure 3 visualises these effects.

Table 1: LMEM results for the effect of LenSWord, STsegment, STseg\_nbr, Cross, Student, HCross and Student on EKS (experiment 1a)

### **6.4 Results of experiment 1a**

The number of words in a segment (**TokS.sg**) had a positive effect on EKS. This might not be too surprising, given that if a translator first reads the whole segment before translating it, the EKS is naturally longer for longer segments. The number of characters in a word (**LenSWord**) had a positive effect. Word frequency also had a similar and highly significant effect on EKS, but only when word length was not included. This is not surprising, given that these two variables covary to a high degree. That word length or frequency should result in longer EKS is to be expected, given that it is more difficult to process long rare words than short frequent words. The sequential numbering of segments in the ST (**STsegment**) had a positive effect on EKS. The likelihood that a word situated further to the end is fixated long before it is translated may lead to this effect. The number of segments in a given ST (**STseg\_nbr**) had a negative effect on EKS.