# Towards Explainable Evaluation Metrics for
Natural Language Generation

Christoph Leiter1, Piyawat Lertvittayakumjorn2, Marina Fomicheva3,
Wei Zhao4, Yang Gao5, Steffen Eger1
1Technische Universität Darmstadt, 2Imperial College London, 3University of
Sheffield
4Heidelberg Institute for Theoretical Studies, 5Google Research

###### Abstract

Unlike classical lexical overlap metrics such as BLEU, most current evaluation
metrics (such as BERTScore or MoverScore) are based on black-box language
models such as BERT or XLM-R. They often achieve strong correlations with
human judgments, but recent research indicates that the lower-quality
classical metrics remain dominant, one of the potential reasons being that
their decision processes are transparent. To foster more widespread acceptance
of the novel high-quality metrics, explainability thus becomes crucial. In
this concept paper, we identify key properties and propose key goals of
_explainable machine translation evaluation metrics_. We also provide a
synthesizing overview over recent approaches for explainable machine
translation metrics and discuss how they relate to those goals and properties.
Further, we conduct own novel experiments, which (among others) find that
current adversarial NLP techniques are unsuitable for automatically
identifying limitations of high-quality black-box evaluation metrics, as they
are not meaning-preserving. Finally, we provide a vision of future approaches
to explainable evaluation metrics and their evaluation. We hope that our work
can help catalyze and guide future research on explainable evaluation metrics
and, mediately, also contribute to better and more transparent text generation
systems.

##  1 Introduction

The field of evaluation metrics for Natural Language Generation (NLG) is
currently in a deep crisis: While multiple high-quality evaluation metrics
(Zhao et al. 2019; Zhang et al. 2020; Rei et al. 2020; Sellam, Das, and Parikh
2020; Yuan, Neubig, and Liu 2021) have been developed in the last few years,
the Natural Language Processing (NLP) community seems reluctant to adopt them
to assess NLG systems (Marie, Fujita, and Rubino 2021; Gehrmann, Clark, and
Sellam 2022). In fact, the empirical investigation of Marie, Fujita, and
Rubino (2021) shows that the vast majority of machine translation (MT) papers
(exclusively) relies on surface-level evaluation metrics like BLEU and ROUGE
(Papineni et al. 2002; Lin 2004) for evaluation, which were invented two
decades ago, and the situation has allegedly even worsened recently. These
surface-level metrics cannot measure semantic similarity of their inputs and
are thus fundamentally flawed, particularly when it comes to assessing the
quality of recent state-of-the-art NLG systems (Peyrard 2019), calling the
credibility of a whole scientific field in question.

We argue that the potential reasons for this neglect of recent high-quality
metrics include: (i) non-enforcement by reviewers; (ii) easier comparison to
previous research, e.g., by copying BLEU-based results from tables of related
work (potentially a pitfall in itself); (iii) computational inefficiency to
run expensive new metrics at large scale; (iv) lack of trust in and
transparency of high-quality black box metrics.

In this work, we concern ourselves with the last named reason,
_explainability_. In recent years, explainability in Artificial Intelligence
(AI) has been developed and studied extensively due to several needs Samek,
Wiegand, and Müller (2018); Vaughan and Wallach (2020). For users of the AI
systems, explanations help them make more informed decisions (especially in
high-stake domains) Sachan et al. (2020); Lertvittayakumjorn et al. (2021),
better understand and hence gain trust of the AI systems Pu and Chen (2006);
Toreini et al. (2020), and even learn from the AI systems to accomplish the
tasks more successfully Mac Aodha et al. (2018); Lai, Liu, and Tan (2020). For
AI system designers and developers, explanations allow them identify the
problems and weaknesses of the system Krause, Perer, and Ng (2016); Han,
Wallace, and Tsvetkov (2020), calibrate the confidence of the system Zhang,
Liao, and Bellamy (2020), and improve the system accordingly Kulesza et al.
(2015); Lertvittayakumjorn and Toni (2021).

Explanability is particularly desirable for evaluation metrics. Sai,
Mohankumar, and Khapra (2020) suggest that explainable NLG metrics should
focus on providing more information than just a single score (such as fluency
or adequacy). Celikyilmaz, Clark, and Gao (2020) stress the need for
explainable evaluation metrics to spot system quality issues and to achieve a
higher trust in the evaluation of NLG systems. Explanations indeed play a
vital role in building trust for new evaluation metrics.111As one illustrating
example, we mention the recent paper of Moosavi et al. (2021) (also personal
communication with the authors). In the paper, the authors express their
distrust for the metric BERTScore applied to a novel text generation task. In
particular, they point out that the BERTScore of a non-sensical output is
0.74, which could be taken as an unreasonably high value. While BERTScore may
indeed be unsuitable for their novel task, the score of 0.74 is meaningless
here, as evaluation metrics may have arbitrary ranges. In fact, BERTScore
typically has a particularly narrow range, so a score of 0.74 even for bad
output may not be surprising. Explainability techniques would be very helpful
in preventing such misunderstandings. For instance, if the explanations for
the scores align well with human reasoning, the metric will likely be better
accepted by the research community. By contrast, if the explanations are
counter-intuitive, users and developers will lower their trust and be alerted
to take additional actions, such as trying to improve the metrics using
insights from the explanations or looking for alternative metrics that are
more trustworthy. Furthermore, explainable metrics can used for other
purposes: e.g., when a metric produces a low score for a given input, the
highlighted words (a widely used method for explanation, see Section 6) in the
input are natural candidates for manual post-editing.

This concept paper aims at providing a systematic overview over the existing
efforts in explainable NLG evaluation metrics and at an outlook for promising
future research directions. We first provide backgrounds on evaluation metrics
(Section 2) and explainability (Section 3), and discuss the goals and
properties of explainable evaluation metrics (Section 4). Then we review and
discuss existing datasets (Section 5) and methods (Section 6) for explainable
metrics, covering both _local_ and _global_ methods for providing
explanations; see Figure 1 for an overview. To better understand the
properties (e.g. faithfulness and plausibility) of different explainable
evaluation metrics, we test and compare multiple representative methods under
three newly-proposed experimental setups and present the results in Section 7.
Our focus in this context is particularly on the relation between
explainability and adversarial techniques, where we examine whether the latter
can automatically identify limitations of existing evaluation metrics, thereby
promoting a better understanding of their weaknesses. At last, we discuss
promising ideas for future work (Section 8) and conclude the paper (Section
9).

We mainly focus on the explainable evaluation metrics for MT in this work, as
it is one of the most representative NLG tasks. Most of our observations and
discussions can easily be adapted to other NLG tasks, however. Source code for
our experiments are publicly available at
https://github.com/Gringham/explainable-metrics-machine-translation.

Figure 1: Current explanations for NLG evaluation metrics fall into three
categories. _Explanation through error highlights_ explains a metric score by
highlighting word-level errors in a translation. _Explanation through
adversarial samples_ checks whether the metrics perform reasonably on
adversarial samples. _Explanation through disentanglement_ splits the metric
score into different components.

##  2 Evaluation Metrics

###  2.1 Metric Dimensions

We first differentiate evaluation metrics along several dimensions. A summary
is shown in Table 1.222We note that categorizations of metrics along many
different dimensions are possible. In the following, we will use the
abbreviation MTE to stand for ‘Machine Translation Evaluation’ (metrics).

Dimension |  Description
---|---
_Type of input_ |  Whether source, reference translation or both are used as benchmark for comparison
_Input representation_ |  Whether the metric relies on words or uses a continuous representation of the input such as word embeddings
_Supervision_ |  The degree of supervision a metric uses
_Granularity_ |  At which level a metric operates: Word-level, sentence-level, document-level
_Quality aspect_ |  What a metric measures: Adequacy, fluency, etc.
_Learning objective_ |  How a metric is induced: Regression, ranking, etc.
_Interpretability_ |  Whether the metric is interpretable
Table 1: A typology of categorizations for evaluation metrics.

#### Type of input

The majority of metrics for MT compare a human reference to an MT hypothesis
(Zhang et al. 2020; Zhao et al. 2019; Colombo et al. 2021; Sellam, Das, and
Parikh 2020). Some metrics directly compare source texts to MT hypotheses
instead (Zhao et al. 2020; Song, Zhao, and Specia 2021; Lo and Larkin 2020),
which is a more difficult task, but eliminates the need for an expert
reference. In contrast, there are also metrics that leverage all three
information signals, i.e., source, reference, and MT hypothesis (Rei et al.
2020), which may lead to better performances when they are all available.

#### Input representation

Embeddings have seen great success in encoding linguistic information as dense
vector representations (Mikolov et al. 2013; Devlin et al. 2018). The
following distinctions of embedding usage can be found in Sai, Mohankumar, and
Khapra (2020):

  * •

No Embeddings: Metrics such as BLEU (Papineni et al. 2002) or NIST (Doddington
2002) are based on exact word matches between n-grams of hypothesis and
reference sentences. METEOR (Lavie, Sagae, and Jayaraman 2004; Banerjee and
Lavie 2005) goes a step further and employs a.o. stemming to account for non-
exact word matches. Nonetheless, all of these metrics do not check whether the
meaning of two sentences is preserved and have difficulties to deal with
paraphrases and synonyms (Ananthakrishnan et al. 2006; Reiter 2018). These
problems lead to a lower correlation with human judgements in segment-level
evaluations (Stanojević et al. 2015; Mathur et al. 2020).

  * •

Static Embeddings: Static embeddings are vector representations such that the
representation of a token will be the same across contexts (Mikolov et al.
2013; Pennington, Socher, and Manning 2014). The use of static embeddings in
MTE allows to compare sentences that use different words than a reference but
express the same content (Tättar and Fishel 2017). Examples of metrics based
on static word embeddings are bleu2vec (Tättar and Fishel 2017) and the
approach of Ng and Abrecht (2015), who induce metrics for summarization.

  * •

Contextualized Embeddings: Contextualized embeddings assign vector
representations depending on an input context (Wang et al. 2020; Devlin et al.
2018). Comparison between sentences in MTE that build on contextualized
representations can be at word-level (Zhang et al. 2020; Zhao et al. 2019) or
by comparing embeddings of the whole sentence (Reimers and Gurevych 2020). The
currently best-performing metrics build on contextualized representations,
e.g. MoverScore (Zhao et al. 2019), BERTScore (Zhang et al. 2020) and
BaryScore (Colombo et al. 2021).

As metrics without embeddings perform a strict comparison between words, we
will refer to them as hard metrics (Zhao et al. 2020). In contrast, we will
refer to metrics that use embeddings as soft metrics.

#### Supervision

This dimension distinguishes whether an automated MTE metric uses a form of
supervision or not.

  * •

Reference-Based: We call a metric reference-based if it requires one or
multiple human reference translations to compare with the hypothesis, which
can be seen as a form of supervision. Such a metric cannot be used in cases
where no bilingual expert is available.

  * •

Reference-Free: Reference-free metrics do not require a reference translation
to grade the hypothesis (Zhao et al. 2020; Ranasinghe, Orasan, and Mitkov
2020; Song, Zhao, and Specia 2021). Instead, they directly compare the source
to the hypothesis. In the literature, reference-free MTE is also referred to
as “reference-less” or (especially in the MT context) “quality estimation”.

Another distinction is whether a metric is untrained or trained, i.e. whether
it has trainable weights that are fitted in order to solve the task of MTE
(Celikyilmaz, Clark, and Gao 2020):

  * •

Not Trainable: This dimension indicates that a metric has no learnable
weights.

  * •

Pre-Trained: Some metrics leverage embeddings that were trained in more
general tasks without fitting them to the task of MTE. This is the case for
BERTScore (Zhang et al. 2020) and MoverScore (Zhao et al. 2019) which extract
contextualized embeddings from language models.

  * •

Fine-Tuned: Finally, some metrics directly optimize the correlation of metric
scores with human judgment in a supervised learning setup. Examples of this
type of metrics include BLEURT (Sellam, Das, and Parikh 2020), COMET (Rei et
al. 2020), and TransQuest (Ranasinghe, Orasan, and Mitkov 2020).

Belouadi and Eger (2022) argue for metrics that use no form of supervision in
order to be as inclusive as possible, i.e., applicable in settings where no
supervision signals are available (e.g., for low-resource and non-English
language pairs).

#### Granularity

Translation quality can be evaluated at different levels of granularity: word-
level, sentence-level and document-level. The majority of metrics for MT give
a single sentence-level score to each input. Beyond individual sentences, a
metric may also score whole documents (multiple sentences) (Zhao, Strube, and
Eger 2022; Jiang et al. 2021), an avenue that is likely to dominate in the
future as text generation systems at document-level will become more common.
In the MT community, metrics that evaluate translations at the word-level
(e.g., whether individual words are correct or not) are also common (Turchi et
al. 2014; Shenoy et al. 2021).

#### Quality aspect

This refers to what a metric evaluates and measures (or is at least compared
to), e.g., human assigned adequacy (via direct assessment (Graham et al. 2016)
obtained from crowd-workers), fluency, or other aspects (such as relevance,
informativeness, or correctness, mostly in other NLG fields). Recently, there
has been a tendency to compare metrics to human scores derived from fine-
grained error analysis (Freitag et al. 2021) as they seemingly correspond
better to human professional translators.

#### Learning objective

Yuan, Neubig, and Liu (2021) describe a further distinction based on the task
a metric is designed to solve (or _how_ a metric is implemented) in order to
achieve high correlation with human judgments. They identify the three tasks
below.

  * •

Unsupervised Matching: They place all metrics that match hypothesis and
reference tokens in this dimension. In terms of the dimension “untrained vs.
trained”, unsupervised matching captures metrics that are “not-trainable” or
“pre-trained”.

  * •

Supervised Regression/Ranking: These models use supervised regression to train
a metric to predict continuous values that directly correlate with human
judgments from hypothesis and reference tokens. Alternatively, a metric may be
trained to rank hypothesis sentences. For example, Rei et al. (2020) propose a
ranking setup in which they minimize a loss that becomes smaller the greater
the distance between embedding representations of the two sentences become.

  * •

Text Generation: The authors place the metric PRISM (Thompson and Post 2020)
and their own metric BARTScore in this condition. BARTScore leverages the fact
that a model that is trained to generate a hypothesis from a reference or
source is at the same time trained to maximize the correlation to human
judgement. They calculate the generation probability of the ground truth given
the input. As metric, they take the arithmetic mean of the loss scores of the
directions reference $\rightarrow$ hypothesis and hypothesis $\rightarrow$
reference.

#### Interpretability

There is a general tradeoff between quality and interpretability of evaluation
metrics: metrics that follow simple formulas such as BLEU and ROUGE are easily
interpretable but of low quality, while high-quality metrics based on
sophisticated language models are typically black box. Aiming for metrics that
satisfy both is the main concern of our paper.

##  3 Explainability and Explanations

In this section, we introduce core concepts of explainable artificial
intelligence (XAI) with a focus on their use in NLP. Generally, XAI aims to
systematically expose complex AI models to humans in a comprehensible manner
Samek et al. (2019). This amounts to _interpreting_ and _explaining_ the
models, and prior work has noted that there is an ambiguous usage of the terms
interpretability and explainability (Barredo Arrieta et al. 2020; Jacovi and
Goldberg 2020). In this paper, we follow the notion of _passive
interpretability_ and _active explainability_ (e.g. Barredo Arrieta et al.
2020; Bodria et al. 2021), as illustrated in Figure 2. A model is explainable
if the model itself or some external instance (described as Explanator in the
figure) can actively provide explanations that make the decision process or
the output clearer to a certain audience. In contrast, a model is
interpretable if it can be passively understood by humans (e.g., decision
trees and k-nearest neighbours).

Figure 2: An illustration of passive interpretability versus active
explainability.

As discussed in Section 2, modern MTE metrics are mostly based on embeddings
and black-box language models, making the metrics become non-interpretable.
Recent work, therefore, tries to generate explanations for the metrics to help
humans better understand them (e.g. Freitag et al. 2021; Fomicheva, Specia,
and Aletras 2021). Hence, the remainder of this section will discuss several
aspects of explanations in XAI and NLP literature, including their scopes,
forms, sources, and evaluations, to serve as an essential background for the
rest of the paper.

###  3.1 Scopes of Explanations

The scope of an explanation indicates the subject that the explanation
explains. Existing surveys (e.g., Doshi-Velez and Kim 2017; Arya et al. 2019a;
Danilevsky et al. 2020; Bodria et al. 2021) classify explanations mainly into
two scopes – local and global explanations.

  * •

Local explanations aim to explain a particular output of the model.
Specifically, given a model $M$ and an input $x$, a local explanation explains
why the model outputs $M(x)$ for the input $x$. Examples of local explanation
methods are Bach et al. (2015); Ribeiro, Singh, and Guestrin (2016); Koh and
Liang (2017); Wallace, Feng, and Boyd-Graber (2018).

  * •

Global explanations aim to explain the working mechanism of the model $M$ in
general, regardless of a specific input. To do so, it may require a set of
(real or synthetic) examples in order to observe and summarize the model
behaviour to be a global explanation. Examples of global explanation methods
are Sushil, Šuster, and Daelemans (2018); Tan et al. (2018); Lundberg et al.
(2020); Setzu et al. (2021).

Besides, Lertvittayakumjorn and Toni (2021) recognize that there are
explanations which stay between the local and the global scopes. These amount
to explanations for groups of examples such as a group of false positives of a
certain class and a cluster of examples with some fixed features. Examples of
XAI work that fall into this middle scope are Chan et al. (2020); Johnson,
Carenini, and Murray (2020).

###  3.2 Forms of Explanations

Explanations for AI models and their predictions can be presented in various
forms. Concerning local explanations, some methods (e.g., Lei, Barzilay, and
Jaakkola 2016; Jain et al. 2020) identify or extract parts in the input $x$
that $M$ considers important when predicting $M(x)$. The extracted parts can
be called the rationales or highlights for $M(x)$. Some methods, instead,
assign relevance scores (also called importance scores or attribution scores)
to all the features (usually tokens) in $x$, showing their relative importance
towards $M(x)$ (e.g., Ribeiro, Singh, and Guestrin 2016; Sundararajan, Taly,
and Yan 2017; Kim et al. 2020). These scores could be visualized using a
saliency map laid on the input $x$. Another way to relate the input to the
predicted output is using decision rules as explanations, showing the
sufficient conditions (satisfied by $x$) for $M$ to predict $M(x)$. For
example, a review $x$ is classified as a positive review by $M$ because of the
decision rule $r:$ If x contains ‘‘best’’ & ‘‘enjoy’’, then M(x) = Positive
with the confidence of 92%. Exemplary Methods using decision rule explanations
are Ribeiro, Singh, and Guestrin (2018a) and Tang and Surdeanu (2021).
Besides, $M(x)$ could also be explained using synthetic or natural input
examples. For example, by using influence functions, Koh and Liang (2017)
present training examples that are most responsible for making $M$ predict
$M(x)$ as explanations. Wachter, Mittelstadt, and Russell (2018) explains
$M(x)$ using a counterfactual example – an (optimized) input $x^{\prime}$ that
is closest to $x$ but has a different prediction (i.e., $M(x^{\prime})\neq
M(x)$). Last but not least, there are also methods producing natural language
texts as explanations for $M(x)$ such as the work by Camburu et al. (2018) and
Liu, Yin, and Wang (2019).

To explain the global behavior of the model $M$, in contrast, there are some
applicable forms of global explanations. One is to train an interpretable
model $S$ to emulate (the behaviour of) the model $M$ and then use $S$ as a
global explanation of $M$. We call $S$ a surrogate model Sushil, Šuster, and
Daelemans (2018); Tan et al. (2018); Lakkaraju et al. (2019). Otherwise, we
may understand the model $M$ by observing a collection of its local
explanations that are diverse enough to cover most regions in the input space
Ribeiro, Singh, and Guestrin (2016, 2018a). Some existing work further
aggregates such local explanations to be a single global explanation of $M$
(e.g., Lundberg et al. 2020; Setzu et al. 2021). Additionally, to facilitate
human understanding, one may also incorporate high-level concepts associated
to the input (e.g., existence of semantic concepts for image classification
(Kim et al. 2018) and syntactic relationships between two input sentences for
sentence-pair classification (McCoy, Pavlick, and Linzen 2019)) into the
explanation. These methods could be categorized as concept attribution methods
(Bodria et al. 2021).

Note, however, that besides the global explanations discussed above, there are
also several analysis methods devised to further understand $M$, e.g.,
analyzing behaviours of individual neurons Li et al. (2016); Poerner, Roth,
and Schütze (2018); Mu and Andreas (2020), probing knowledge stored in the
model Hewitt and Manning (2019); Voita and Titov (2020); Belinkov (2021),
testing model behaviour in many (challenging) scenarios Poliak et al. (2018);
Wang et al. (2019); Ribeiro et al. (2020a), searching for adversarial rules
where the model often fails Ribeiro, Singh, and Guestrin (2018b); Wallace et
al. (2019), etc. Though it is arguable whether these analysis methods are
producing explanations for the model, they do provide lots of useful global
insights into the model. So, we group them under global techniques to be
discussed, specifically for MTE, in Section 6.2.

###  3.3 Sources of Explanations

In a specific situation, ways to obtain explanations depend largely on the
category of the model $M$. Here, we list the three main categories.

  * •

Interpretable models. Some machine learning models are easy to understand for
humans such as Naive Bayes, decision trees, linear regression, and k-nearest
neighbours Freitas (2014). Existing works, therefore, call them interpretable
Arya et al. (2019b) or self-explaining Danilevsky et al. (2020). We can
normally obtain local explanations of these models at the same time as
predictions (e.g., the corresponding path in the decision tree), while the
models can be considered global explanations of themselves (e.g., the trained
decision tree itself).

  * •

Explainable models by design. Some models are not interpretable, still they
are able to output local explanations for their predictions by design. For
instance, Narang et al. (2020) train a black-box sequence-to-sequence model to
perform a set of tasks and also output corresponding explanations if
requested. Jain et al. (2020) train a black-box model to extract a rationale
from a given input first and then feed the rationale into another model to
predict the output. So, the extracted rationale can be used as a local
explanation for the prediction. However, because these models are not
interpretable, we cannot use themselves as global explanations. Therefore, we
require a post-hoc global explanation method in this case.

  * •

Black-box models. These models are not interpretable and do not produce
explanations for their predictions. Therefore, they are completely black-boxes
and require a separate step to extract (local or global) explanations from
them in particular. Methods for extracting explanations in this case are
called post-hoc explanation methods. Some of them can be applied to any models
(i.e., being model-agnostic) such as SHAP Lundberg and Lee (2017) and LIME
Ribeiro, Singh, and Guestrin (2016), whereas others are applicable to a
specific class of models (i.e., being model-specific) such as Tree SHAP
Lundberg, Erion, and Lee (2018) for tree-based models and LRP Bach et al.
(2015) for neural networks.

Note that, although post-hoc explanation methods are often used with black-box
models, they can be applied to models from the first two categories to
generate alternative explanations.

###  3.4 Evaluation of Explanations

In general, there are numerous methods to evaluate explanations in XAI
literature. Lertvittayakumjorn (2021) divides them into two broad categories,
i.e., intrinsic evaluation and extrinsic evaluation. While the former is
concerned with evaluating desirable properties of explanations, the latter is
concerned with evaluating the usefulness of the explanations in a downstream
task, which usually requires a combination of several intrinsic properties. In
this section, we will introduce two intrinsic properties of explanations,
faithfulness and plausibility, together with existing methods for evaluating
them, seeing that these two properties are relevant to the context of
explainable MTE metrics.

#### Faithfulness

An explanation is faithful if it accurately represents the reasoning process
underlying the model’s prediction(s) Jacovi and Goldberg (2020). In other
words, faithfulness focuses on helping humans understand how the model $M$
works. Existing faithfulness evaluation methods could be categorized into
three groups with regards to the ground truths that we compare the
explanations against.

  * •

Known ground truths. For an interpretable model, we can treat the model’s
decision making mechanism, which are transparent to humans, as a ground truth
for evaluating faithfulness of an alternative explanation. For instance, if we
explain a prediction of a linear regression model, the attribution score of a
feature should be consistent with the product of the feature and the
associated weight in the model. This could be seen as a sanity check for
faithfulness of the explanation method.

  * •

Inferred ground truths. In some situations, the ground truths may be inferred
even though the model is complex. For example, Poerner, Schütze, and Roth
(2018) work on the subject-verb agreement task, predicting if the verb of a
sentence is plural or singular based on the preceding texts. Because the LSTM
model they use achieves beyond 99% accuracy, they infer that the model is
always looking at the right word in the sentence (i.e., the corresponding
subject) when making a prediction. As this subject can be accurately
identified using a syntactic parser, they use the subject as an inferred
ground truth which the explanation should assign the highest relevance score
to.

  * •

Unknown ground truths. Without any ground truths, some evaluation methods,
instead, check whether the target model behaves in the same way as the
explanation says. Lertvittayakumjorn (2021) considers this as a looser
definition of faithfulness. To evaluate relevance scores, for example, we can
gradually remove or replace input words starting from the ones with high
relevance scores to the ones with low relevance scores and then observe how
the predicted probability changes Arras et al. (2016); Nguyen (2018); Kim et
al. (2020). If the probability drops significantly when the words with high
relevance scores are perturbed, we can consider the relevance scores faithful.
The area over the perturbation curve (AOPC) score, which is a metric
introduced by Samek et al. (2017) in the domain of computer vision, can be
used to quantify faithfulness for this evaluation method. A very similar
approach is called degradation tests (Schulz et al. 2020; Chen et al. 2021).
These additionally consider the least relevant perturbations and are based on
the area between the perturbation curves. Instead of observing predicted
probability, Chen and Ji (2020) measure how many of the class predictions
remain the same if only $k$ most relevant tokens (according to the relevance
scores) is kept in the input. This results in a metric called post-hoc
accuracy.

In any case, because faithfulness concerns the accuracy of the explanations
with respect to the underlying model, Jacovi and Goldberg (2020) emphasize
that we should not involve humans into the evaluation process.

#### Plausibility

In contrast to faithfulness, plausibility involves humans into the evaluation
process. Specifically, an explanation is plausible if it is convincing to
humans towards the model prediction Jacovi and Goldberg (2021). Even an
explanation for an incorrect prediction or an explanation that is not faithful
to the underlying model can be plausible as long as human judgments say that
it supports well the model prediction. Therefore, one way to measure
plausibility of an explanation is to compare it with human explanation(s). A
machine explanation that has higher correlation with human explanation(s) is
considered more plausible. This idea was implemented by Mohseni, Block, and
Ragan (2018). As another example, Lertvittayakumjorn and Toni (2019) select
samples where a well-trained model has a high classification confidence. Then,
for each of the selected samples, they apply an explanation method and present
the most important features to humans (without showing the input text). If the
humans correctly predict the output of the model, it implies that the
explanation is plausible and well justifying the model output from human
perspectives.

##  4 Explainable MT evaluation

In this section, we discuss main goals of explainable MT evaluation and define
core properties that set this task apart from other tasks.

###  4.1 Goals of explainable MT evaluation

Different goals are sought to be fulfilled by explainability techniques
(Lipton 2016; Carvalho, Pereira, and Cardoso 2019; Barredo Arrieta et al.
2020) as discussed in the Introduction section.

Description | Use Cases | Subgoals
---|---|---
Diagnose and improve metrics |  \- Build better MT systems |  \- Informativeness \- Transferability
More expressive and accessible metrics |  \- Build researcher’s trust \- Metric usage in new scenarios |  \- Accessibility \- Transferability
Support Semi-Automatic labeling |  \- Faster annotation |  \- Informativeness \- Interactivity
Checking for social biases | \-  Ensure fair MT models and metrics |  \- Fair & Ethical Decision Making \- Informativeness \- Trust
Table 2: Goals of explainable Machine Translation evaluation. The subgoals are
described by Lipton (2016); Barredo Arrieta et al. (2020).

Lipton (2016) and Barredo Arrieta et al. (2020) define common goals in the
development of explainable systems: Informativeness, Transferability,
Accessibility, Interactivity, Fair & Ethical Decision Making and Trust.
Informativeness seeks to increase the amount of information conveyed by a
model and about its solution process. Transferability is the goal of applying
a model to new domains. Accessibility aims to enable better usage by non-
experts. Interactivity seeks to improve user-experience by using a model in an
interactive setup. Fair & Ethical Decision Making is the goal of providing
bias-free systems. Lastly, Trust is an often subjective goal of achieving
trust in a models behavior.

Here, we consolidate four main goals for explainable MT evaluation and
associate them with the common goals by Lipton (2016) and Barredo Arrieta et
al. (2020), as displayed in Table 2.333As one main goal for explainable MT
evaluation can associate with several common goals, we call the common goals
subgoals in the table. We follow with a short description of each of them
below.

  * •

Diagnose and improve metrics: By explaining why a metric predicted a certain
score for a machine translation, where humans would assign a different score,
the weaknesses of an existing metric might be understood. This might enable
architectural changes or the selection of different datasets leading to metric
improvement. Likewise, explaining the whole model can unveil if the metric
follows desired general properties (see Section 6.2) or might otherwise be led
astray by carefully crafted adversarial input. This goal encapsulates sub-
goals such as Informativeness and Transferability (e.g. Lipton 2016; Barredo
Arrieta et al. 2020). Freitag, Grangier, and Caswell (2020) show that metrics
can also include biases towards reference sentences, i.e., a preference
towards REF, even though there are other correct translation options. They
also state that references would often include Translationese, i.e., source
artifacts that originate when human translators do not find a natural
translation. We count the quantification of these problems to diagnosis and
metric improvement as well.

  * •

Make metrics more expressive and accessible: A metric that assigns a score on
a sentence level is difficult to understand for non-experts, e.g. non-native
speakers. If a metric provides further information such as which words it
considers incorrect, it may become more accessible. Similarly, for an expert
it might save time and discussions to be presented with such explanations.
This use case involves the goals of Informativeness and Accessibility (e.g.
Lipton 2016; Barredo Arrieta et al. 2020). Further, we hypothesize that
accessibility plays an important role in metric selection and might be one of
the reasons for the broad usage of BLEU. In other words, researchers might
have built trust in BLEU due to its long and wide usage. Hence, when
explanations help build trust into black-box metrics, the paradigm shift to
newer metrics might be propelled.

  * •

Support Semi-Automatic labeling: The manual labeling process of data is
expensive. Thereby, fine-grained annotations like word-level translation error
detection (e.g. Fomicheva et al. 2021) are specifically difficult to obtain.
Many data-labeling platforms support semi-automated labeling,444e.g.
https://datasaur.ai/ i.e. pipelines, in which human annotators only need to
check and correct automatic predictions. Likewise, obtaining automatic
explanations could boost efficiency of the annotators in this use case.

  * •

Checking for social biases: Learned metrics might be biased towards certain
types of texts used during training. Biases could be detected by observing
explanations where sensitive attributes (e.g., gender, political or racial
aspects) are flagged to be important (Lipton 2016). For example, explanations
could show that a metric considers male names more important for a translation
than female names in the same scenario. Mitigating such biases involves goals
such as Fair & Ethical Decision Making, Informativeness and Trust (e.g. Lipton
2016; Barredo Arrieta et al. 2020).

###  4.2 Properties of explainable MT evaluation

Property | Short Description
---|---
Multiple Inputs |  MT metrics take interdependent hypothesis- and source-/reference- sentences as input
Relation to translation errors |  Explanations of MT metrics are related to translation error detection
Multilingual Inputs |  Reference-free evaluation is one of few tasks with multilingual inputs
Output Scores |  Machine translation metrics return numeric outputs (in contrast to classification models)
Varying Scales |  The distribution of metric scores can vary across metrics
Label preservation $=$ Meaning preservation |  Adversarial attacks need to preserve meaning
Table 3: Properties affecting the explainability of MT evaluation metrics.

Next, we consider, which factors of MT metrics distinguish them from other
metrics and models in general and discuss the impact this has on their
explainability. Table 3 shows an overview of these properties. Now, we
describe them in more detail:

  * •

Multiple Inputs: MT metrics take multiple interdependent inputs (hypothesis,
source and/or reference(s)). Machine translations should fulfill the
properties of fluency and adequacy, and metrics should evaluate these (e.g.
Yuan, Neubig, and Liu 2021), i.e. the translation should be fluent - without
grammatical errors and easy to read -, and adequate - without adding, losing
or changing information. Each word in a correct translation depends on the
source sentence and each word in the source sentence should be reflected in
the translation (not necessarily one-to-one). As reference sentences are also
translations of the source, the contents of references should also be
reflected in the hypothesis sentence and vice-versa. As MT metrics take
multiple inputs, there are different options of which parts of the input are
explained when local (see Section 3.1) explainability techniques are applied:

    * –

Explaining with respect to the Hypothesis: Usually when Machine Translation is
evaluated, the reference and/or source sentences are already given, for
example, by a human annotator. Then, the metric can be used to generate a
score that answers the question “Is the hypothesis good with respect to the
given source or reference?”. Explaining with respect to the hypothesis would
additionally (try to) answer the questions “Why did the metric assign a
certain score for this hypothesis with respect to the given source or
reference?” and “How do changes to the hypothesis affect the score?”.

    * –

Explaining with respect to Source/Reference: This would answer the additional
questions of “Why did the metric assign a certain score for this source or
reference with respect to a given hypothesis?” and “How can we change the
reference/source to affect the score in certain ways?”. Usually the source
and/or reference(s) are considered the ground truth of an evaluation.
Therefore, it is not necessary to change them, as long as the integrity of the
data is not questioned. They can however be interesting for model diagnosis to
see whether the explained metric considers the expected parts of a
source/reference when grading the hypothesis.

    * –

Explaining with respect to all inputs: The question of whether all inputs can
be explained together largely depends on the choice of the explainability
method. For example, for Input Marginalization (Kim et al. 2020), the result
will be the same as when the Hypothesis and Source/Reference are explained
solely. This is because this technique explains each word solely while keeping
all other parts of the input fixed. However, for additive feature explanation
methods such as SHAP (Lundberg and Lee 2017) and LIME (Ribeiro, Singh, and
Guestrin 2016), the goal is to find a contribution of each feature to the
final score. Therefore, when a SRC/REF and HYP are explained together by this
technique, the scores assigned to each of their tokens will contribute to the
final score together. This is not desirable as it might lead to corresponding
tokens between HYP and SRC/REF to be assigned with contradicting feature
importance (one will be positive while the other is negative). Additionally,
doing this would invert the baseline of the attributions. As an example,
assume we want to explain the reference-based metric score for a reference and
a hypothesis. When we explain only the hypothesis, as described in the first
bullet point, a baseline contribution could be determined by removing all
words from the hypothesis (depending on the technique). Hence, the metric
score is probably low, as it compares an empty sentence to a non-empty
sentence. However, when the feature importance of reference and hypothesis is
determined together, the baseline would be determined by comparing an empty
hypothesis to an empty reference, leading to a high baseline score.

  * •

Relation to translation errors: As adequacy is a key goal of MT (e.g. Yuan,
Neubig, and Liu 2021), metrics should have lower scores when it is violated,
i.e. if there are translation errors. Hence, it is intuitive that explanations
of performant metrics should consider translation errors as important (or
harmful to a metric’s score).

  * •

Multilingual Input: Reference-free metrics have multilingual inputs. While
there are other tasks with multilingual inputs, such as cross-lingual
summarization (Zhu et al. 2019) and cross-lingual semantic textual similarity
(Cer et al. 2017a), it is not common to many tasks and therefore should be
considered when explaining them.

  * •

Output Scores: Most MT evaluation metrics return numeric output scores;
however, many explainability techniques in NLP have originally been used with
classification. The same is true for the closely related field of adversarial
attacks (see our Section 7.1). While some of them can be adapted to explain
numeric scores easily (e.g., LIME (Ribeiro, Singh, and Guestrin 2016)), others
require the definition of intervals for which certain conditions should be met
(e.g., Anchor (Ribeiro, Singh, and Guestrin 2018a)).

  * •

Varying Scales: As many metrics are based on learned models, the outputs often
are not fixed to a range between 0 and 1. Therefore, they cannot be directly
interpreted as probabilities. This problem can be tackled by using an approach
detailed in Zhang et al. (2020). Here, the authors determine a lower boundary
of the metric by averaging the scores of a large collection of dissimilar
sentences. Then, they rescale all further computations of the metric with this
baseline. However, while this leads to the score to be between 0 and 1 most of
the times, it does not guarantee for it.

Another option is to normalize the scores to have a mean of 0 and a standard
deviation of 1 (z-score) (Kaster, Zhao, and Eger 2021). For a given dataset
this guarantees that scores are centred around 0 and spaced comparably across
metrics. However, the normalized scores of new or perturbed sentences might
follow a different distribution and fall out of this range. Additionally, the
outputs of the metrics could be skewed differently. For instance, for two
metrics that return outputs between 0 and 1, one could be more pessimistic and
usually assign lower scores than the other. This makes comparison difficult
as, for example, a score of 0.7 can have a different meaning for two metrics.

  * •

Label preservation $=$ Meaning preservation: In general, adversarial attacks
are label-preserving modifications of an input (designed to expose limitations
of models). In MT, the concept of label-preservation coincides with the notion
of meaning-preservation (via the concept of ‘adequacy’ that MT metrics
typically measure). This requires stronger adversarial attack methods; see our
Section 7.

##  5 Datasets

Explainability datasets usually include human explanations for a certain task
(e.g., text classification). The plausibility of machine generated
explanations can then be evaluated by comparing them against the ground
truth.555In this Section we focus on the plausibility aspect of explanations,
see Section 3.4 for the discussion of faithfulness. Three types of ground
truth explanations are typically considered: highlights (also known as
rationales), free-text explanations and structured explanations Wiegreffe and
Marasovic (2021). Based on the terminology we introduced in Section 3,
highlights are _feature importance explanations_ , where the most important
tokens are selected as an explanation. Free-text explanations and structured
explanations are both _textual explanations_ , but structured explanations
have constraints on the form of the text.

With the exception of the recent work by Fomicheva et al. (2021), there are no
datasets devised explicitly for studying explainability of MT evaluation
metrics. However, depending on the type and definition of explanations some of
the existing datasets for MT evaluation can be leveraged for this purpose.

As mentioned in Section 4.2, one of the interesting properties of the MT
evaluation task is that erroneous words in the MT output serve as explanations
for sentence-level or system-level quality scores. Table 4 provides an example
of the three types of explanations mentioned above for sentence-level MT
evaluation.

Data |  Explanation
---|---
(Source) Pronksiajal voeti kasutusele pronksist tooriistad, ent kaepidemed
valmistati ikka puidust (MT) Bronking tools were introduced during, but
handholds were still made up of wood.
Score: 0.3 |  Highlights: (Source:) Pronksiajal voeti kasutusele pronksist tooriistad, ent kaepidemed valmistati ikka puidust (MT) Bronking tools were introduced during the long term, but handholds were still made up of wood
Free-text: MT quality is low because it contains two lexical errors and one
omission error.
Structured: 2 lexical errors, 1 omission error
Table 4: Example of highlight-based, free-text and structured explanations for
MT evaluation of a sentence translated from Estonian into English.

Defining explanations for MT evaluation as translation errors allows to
leverage a wide variety of existing MT evaluation datasets to study
explainability.

#### Post-editing based datasets

A popular method for obtaining silver labels for MT evaluation is post-
editing. Post-editing based datasets contain sources sentences, machine
translations and the corresponding human post-edits (PEs) Fomicheva et al.
(2020a). Sentence-level quality scores are computed as the minimum distance
between the MT and the PE (the so called HTER score Snover et al. (2006)),
whereas word-level labels can be derived from the same data by computing the
minimum distance alignment between the MT and its post-edited version. Thus,
the misaligned words in the MT naturally provide a ground truth explanation
for the sentence-level score. An important disadvantage of these datasets,
however, is the noise introduced by the automatically computing the MT-PE
alignment.

#### Error annotation

Another type of dataset where both word and sentence-level scores are
available are the datasets based on manual error annotation. MT error
annotation protocols such as MQM (Multidimensional Quality Metrics) (Lommel,
Burchardt, and Uszkoreit 2014, 2015) frame MT evaluation as an error
annotation task. Each word in the MT output is assigned a quality label based
on a fine-grained error typology. The sentence-level score is then derived as
a weighted average of word-level errors. Thus, in this case the error labels
can be used as explanations for a metric that outputs sentence-level MQM
scores. Note that this type of data would also allow for explanations that
involve the type of error, as illustrated in the free-text and structured
explanation in Table 4. The disadvantage of this annotation scheme is low
inter-annotator agreement and high annotation costs.

#### X-QE dataset

Few datasets employ human annotated rationales as explanations for machine
translation evaluation. The Eval4NLP 2021 shared task (Fomicheva et al. 2021;
Gao et al. 2021) provided the first dataset that jointly annotated sentence-
level scores with word-level error highlights (seen as explanations for the
sentence-level scores) for the MT setting. Another related dataset consists of
annotations collected in the domain of crosslingual divergence (Briakou and
Carpuat 2020).

Overall, existing datasets can be used to evaluate the plausibility aspect of
explanations for MT evaluation systems by leveraging the relation between
word-level and sentence-level quality. However, the error-based definition
discussed in this Section has two important limitations. On the one hand, it
is not clear what the highlight-based explanations should look like for high-
quality MT. On the other hand, it is not clear what the explanations should
look like for an MT evaluation system that operates at word level (i.e.
predicts translation errors).

##  6 Taxonomy of Existing Explainable Evaluation Metrics

Work | Type | L/G | Method | A/S | Goals
---|---|---|---|---|---
Eval4NLP 2021: (Fomicheva et al. 2021) |  |  | Various |  |
Rubino, Fujita, and Marie (2021) | FI | L | Expl. by Design | S | A/L
Treviso et al. (2021) | FI | L | Various | A/S | A/L
SemEval 15/16: (Agirre et al. 2015, 2016) |  |  | Various |  |
Magnolini, Feltracco, and Magnini (2016) | CAl | L | Neural Netw. | S | A/E
Yuan, Neubig, and Liu (2021) | CA | L | Generation Prob. | S | E
Adversarial Attacks (Section 7) | EbE | L | Perturbations | A | D/E
Kaster, Zhao, and Eger (2021) | EbS/CA | G | Linear Regr. | A | D/E
Sai et al. (2021) | CA | G | Perturbations | A | B/D/E

Table 5: Explainability for MT metrics. We distinguish the explanation types
Concept Attribution (CA), Chunk Alignment (CAl), Feature Importance (FI),
Explanation by Example (EbE) and Explanation by Simplification (EbS). L/G
considers Local (L) vs Global (G) and A/S considers (A)gnostic vs (S)pecific.
The column “Goals” specifies which of the goals of Section 4 can be addressed
with the respective techniques. Thereby, we consider (B)ias detection, metric
(D)iagnosis, metric (E)xpressiveness and automated (L)abeling.

In this section, we categorize and summarize recent approaches related to the
concept of explainable MT evaluation. Based on the dimensions we introduced in
Section 3, we describe the techniques themselves and discuss how they relate
to the properties of explainable MT evaluation introduced in Section 4. In
Table 6, we provide an overview table of our taxonomy.

###  6.1 Local Techniques

As described in the previous section, local explainability techniques provide
additional information that helps understand model behavior with specific
input/output pairs.

#### Word-Level Feature Importance Explanations

When humans evaluate translations, they often focus on the errors that they
can identify on a word- or phrase-level (Freitag et al. 2021). Fomicheva,
Specia, and Aletras (2021) and the 2021 Eval4NLP shared task (Fomicheva et al.
2021) build on this idea and evaluate how well feature importance is
correlated with human word-level error annotations. They use this correlation
as an indicator of plausibility (see Section 3.4). This follows the intuition
that showing word-level errors as an explanation is plausible to humans, who
look for the same kind of clues. In other words, word-level error extraction
from sentence-level metrics could be used as additional benchmark for
explainability methods. Fomicheva, Specia, and Aletras (2021) explore this
approach with the supervised reference-free metric TransQuest (Ranasinghe,
Orasan, and Mitkov 2020). They manually label correct words with 0 and
incorrect words with 1 in the MT hypothesis. As feature importance scores are
continuous rather than binary, they use the metrics area under the receiver
operator characteristic curve (AUC), average precision (AP), recall at top-k
(R@K) and accuracy at 1 (A@1) to compare their manual annotation to automatic
feature importance scores. Fomicheva, Specia, and Aletras (2021) choose four
well-known explainability techniques in order to extract the feature
importance scores, (i) LIME (Ribeiro, Singh, and Guestrin 2016), (ii)
information bottleneck (Schulz et al. 2020), (iii) integrated gradients
(Sundararajan, Taly, and Yan 2017) and (iv) attention (e.g. Wiegreffe and
Pinter 2019; Serrano and Smith 2019). Additionally, Fomicheva, Specia, and
Aletras (2021) compare a metric that was trained with supervision to compute
word-level errors in a classification setting (Ranasinghe, Orasan, and Mitkov
2021) to a glass-box method that uses the word-level translation probabilities
of each word of the known MT model as feature importance (Fomicheva et al.
2020b). The following key findings are reported:

  * •

LIME performs worse than the other methods, which is attributed to the fact
that LIME works with perturbations of the input rather than at a hidden state.
Perturbations on input are not suitable method when explaining MT evaluation
since removing an erroneous word does not make the sentence correct.

  * •

Feature importance for word-level explanation performs competitively to the
glass-box method, and integrated gradients even approaches the performance of
the supervised metric.

The 2021 Eval4NLP (Fomicheva et al. 2021) shared task explores a very similar
evaluation approach of plausibility as Fomicheva, Specia, and Aletras (2021).
For training and development phases, they provide extracts from MLQE-PE.
However, as test set they provide a novel dataset that contains directly
annotated word-level labels (see Section 5). As baseline systems, the
organizers provide a random baseline, as well as TransQuest explained with the
model-agnostic LIME and XMoverScore (Zhao et al. 2020) explained with the
model-agnostic SHAP (Lundberg and Lee 2017). Seven systems were submitted to
the shared task, three of which leveraged word-level supervision: one system
with synthetic data (Rubino, Fujita, and Marie 2021) and two with manually
annotated data (Treviso et al. 2021; Polák, Singh, and Bojar 2021). In the
following, we present a short summary of the two best performing submissions:

  * •

Error identification with metric embeddings and attention. The approach by
Rubino, Fujita, and Marie (2021) jointly fine-tunes an XLMR model with a so-
called metric embedding matrix. For each pair of input sentences, multiple
standard metrics (e.g. BLEU and CHRF) are computed and the resulting scores
are multiplied with the matrix. This leads to a metric embedding, a learned
vector representation of the metric results. They then leverage the metric
embedding in an attention mechanism with the hidden states of the XLMR model
in order to learn which parts of an input which metrics focus on. They then
leverage the attention weights in the computation of sentence- and word-level
scores. This approach is a local explainability technique that is explainable
by design.

  * •

Scaled attention weights. Treviso et al. (2021) fine-tune 2 XLMR models
(Conneau et al. 2020) and a RemBERT (Chung et al. 2021) for sentence-level
quality estimation. They then explore various explainability techniques to
extract meaningful word-level scores. Specifically, they explored 3 attention
mechanisms, (a) using the row-wise average across all attention heads, (b)
only averaging promising rows, (c) scaled by the norm of value vectors
(Kobayashi et al. 2020). They also explore gradient-based methods (i) using
the gradient of hidden states multiplied with the hidden states, (ii) using
the gradient of hidden states multiplied with the attention weights, (iii)
using Integrated Gradients (Sundararajan, Taly, and Yan 2017). Finally, they
employ a method that learns binary masks of input features by using a second
model (Bastings, Aziz, and Titov 2019). They achieve their best results for an
unsupervised setting with the normalized attention.

Limitations: First, a potential issue with the evaluation approach of Eval4NLP
is that it does not consider the property of faithfulness (see Section 3.4).
Hence, there is no guarantee that the extracted word-level scores actually
reflect the sentence-level score, i.e., explain the sentence-level metric
scores. Second, certain translation errors cannot be easily captured by
highlighting specific words. For example, the annotation scheme cannot handle
cases where the MT fails to explicitly express a grammatical feature that is
omitted in the source language but is required to be explicit in the target
language (e.g. the use of articles when translating from Russian into German).
Third, different translation errors affect the sentence score to a varying
extent, which cannot be properly captured with binary labels. Fourth, the
approach in Eval4NLP does not provide correspondence between highlighted error
words in the source and target language. Finally, ranking the participating
systems according to their global independent statistics might be unreliable,
as we discuss in Section 7.3.

#### Chunk Alignment

For the field of semantic textual similarity (STS), which is very closely
related to evaluation metrics, more fine-grained forms of local explainability
have been explored. The second tasks of SemEval-2015 (Agirre et al. 2015) and
-2016 (Agirre et al. 2016) ask participants to perform a labeled chunk
alignment between two sentences as explanation for STS. In the respective
datasets, they annotate how phrases relate between sentences and assign scores
to the relation strength based on a 0 to 5 scale. Also, they assign labels
such as “similar” to define the type of the relation. As example, the authors
consider the sentences

$\displaystyle s_{1}$ $\displaystyle=``\text{12 killed in bus accident in
Pakistan}^{\prime\prime}$ $\displaystyle s_{2}$ $\displaystyle=``\text{10
killed in road accident in NW Pakistan}^{\prime\prime}$

They show sample alignments for “[12] $\Leftrightarrow$ [10]” to be “similar”
with a relation strength of 4 and “[in bus accident] $\Leftrightarrow$ [in
road accident]” to be a “specification” with relation strength of 4. The
annotation process first assigns chunks, then relation strength and then the
label(s). To measure the system quality, they use different F1 measures taking
into account the scores and labels. Their baseline system uses rules to assign
the label. The best performing system at that time used a multi-layer
perceptron and multi-task learning (Magnolini, Feltracco, and Magnini 2016).
There are recent works that improve on the task, especially the phrase-level
alignment. For example Lan, Jiang, and Xu (2021); Arase and Tsujii (2020)
provide new datasets and approaches for this topic.

Limitations: The approach requires more fine-grained annotation, which would
result in lower agreement levels among annotators or less reliable automatic
annotation. The relation of the annotation to translation errors, as a key
factor in explainable MT metrics, is also not explicit in the scheme.

#### Generation Direction

The recently proposed metric BARTScore by Yuan, Neubig, and Liu (2021) treats
NLG evaluation as a generation task. They use BART (Lewis et al. 2020) for
text generation to predict the probability of a sentence to be generated given
another sentence. The resulting probability is used as metric score. Yuan,
Neubig, and Liu (2021) evaluate different generation directions: Faithfulness:
SRC$\rightarrow$HYP, Precision: REF$\rightarrow$HYP, Recall:
HYP$\rightarrow$REF, F-Score: HYP$\leftrightarrow$REF (the arithmetic average
of precision and recall). For example, for SRC$\rightarrow$HYP the metric
score is the probability that BART generates the HYP given the SRC. They state
that these directions would encapsulate different evaluation perspectives.
E.g. HYP$\rightarrow$REF would encapsulate semantic coverage, i.e. how well
the meaning of the reference is captured by the hypothesis. As such, providing
the results of different generation directions can be treated as
explainability approach that provides additional information on a sample
instead of a single metric score.

Based on the evaluation directions presented by (Yuan, Neubig, and Liu 2021),
the recent ExplainaBoard webtool (Liu et al. 2021) provides a possibility to
compare and benchmark evaluation metrics. This dimension is provided under the
title “Meta Evaluation for Automated Metrics”.

Limitations: The provided scores of different generation directions may not
readily be meaningful to different users.

###  6.2 Global Techniques

As discussed in the previous sections, most explainability techniques for
neural networks consider local explanations. While these can provide an
insight into a model’s decision process for specific samples, often it also is
desirable to characterize how the model will behave in general. In this
section, we consider recent approaches of global explainability.

#### Disentanglement along Linguistic Factors

Kaster, Zhao, and Eger (2021) propose a global explainability technique that
decomposes the score of sentence-level BERT-based metric into different
linguistic factors. In particular, they explore the degree to which metrics
consider each of the properties syntax, semantics, morphology and lexical
overlap. As a first step, they define several explanatory variables:

  * •

Lexical Overlap Score (LEX): The lexical overlap score between a hypothesis
and a reference/source is determined using BLEU (Papineni et al. 2002) with
unigrams, thus ignoring word order. For reference-free metrics, they generate
pseudo-references by translating the source sentences with Google Translate
before applying BLEU.

  * •

Morphological Score (MOR): The morphological score computes the cosine
similarity of morphologically enriched sentence embeddings. These are based on
word embeddings finetuned on morphological lexicons following Faruqui et al.
(2015).

  * •

Semantic Score (SEM): The semantic score leverages human annotated sentence
level scores (adequacy or semantic similarity) provided for different
datasets.

  * •

Syntactic Score (SYN): The syntactic score measures the syntactic similarity
between hypothesis and source/reference with the tree edit distance (TED)
(Bille 2005) of parse trees generated by the Stanford dependency parser (Chen
and Cherry 2014).

Kaster, Zhao, and Eger (2021) apply z-score normalization to make the scores
of these explanatory variables comparable. Table 6 shows an example they use
to demonstrate the different scores for two sentence pairs.

Hypothesis ($y$) |  Reference/Source ($x$) | SEM | SYN | LEX | MOR
---|---|---|---|---|---
It is a boy , likes to sport , but it cannot do it because of their very. |  He is a boy, he likes sports but he can’t take part because of his knee. | -1.57 | 0.98 | -0.59 | -0.87
Zwei Besatzungsmitglieder galten als vermisst. |  Two crew members were regarded as missing. | 0.83 | 0.99 | 0.46 | -2.40
Table 6: Example setups with normalized semantic, syntactic, lexical overlap
and morphological scores (Kaster, Zhao, and Eger 2021).

They then estimate the following linear regression:
m(x,y)=α⋅sem(x,y)+β⋅syn(x,y)+γ⋅lex(x,y)+δ⋅mor(x,y)+η where $x$ is a reference
or source sentence, $y$ is a hypothesis sentence and $sem$, $syn$, $lex$ and
$mor$ are the scores of the respective linguistic properties. $m$ is the
metric that is explained, $\eta$ is an error term and $\alpha$, $\beta$,
$\gamma$ and $\delta$ are learned weights that indicate the linear influence
each property has on the metric score. The degree to which the learned
regressors approximate the metric function is determined with the
determination coefficient $R^{2}$.

They conduct experiments with data from WMT15-WMT17 (Stanojević et al. 2015;
Bojar et al. 2016; Bojar, Graham, and Kamran 2017) for the domain of machine
translation and STSB (Cer et al. 2017b) for the domain of semantic textual
similarity. The following key findings are reported:

  * •

The $R^{2}$ value is generally higher for reference-based metrics than for
reference-free metrics, implying that the learned linear regression can better
explain reference-based metrics. Kaster, Zhao, and Eger (2021) hypothesize
that this is due to missing explanatory variables (regressors) or non-linear
relationships. They introduce a fifth property, cross-lingual bias, whose
“ground truth” is given by the scores a reference-free metric returns in a
reference-based setup. The reference is computed with Google Translate.
Including this factor with an additional regressor improves the $R^{2}$ score
however only in some settings.

  * •

Each metric captures semantic similarity and lexical overlap to some degree.
Syntactic and morphological similarity are either captured to a smaller extent
or are even negatively correlated with the metric score. Especially,
MoverScore and BERTScore have a comparatively high coefficient for the lexical
score.

  * •

Based on the finding that the metrics favor lexical overlap, Kaster, Zhao, and
Eger (2021) explore their robustness towards particular adversarial examples
that preserve lexical overlap but change meaning. They show that non-
paraphrases that have a high lexical overlap but do not preserve semantics
tend to achieve better scores than paraphrases with low lexical overlap. This
exposes an important weakness of the metrics.

In terms of the dimensions introduced in Section 3, we can further categorize
this explainability technique as “explanation by simplification”, as a simple
linear model is learned that explains the complex metric score as a linear
combination of scores that can be more easily understood. We also point out
that it can be used as a local explainability techniques, as it also explains
individual instances.

Limitations: In terms of goodness-of-fit, the approach could not explain
reference-free metrics well, so plausibly requires alternate explanatory
variables. The search for such regressors may be inspired by other text
generation tasks (such as summarization) where not a global metric score is
reported but several scores (such as coherence, fluency, etc.). These could
then decompose a global MT score; cf. the discussion in Sai et al. (2021)
where they argue against one global score for evaluating NLG. Further, SEM,
which is determined by humans, and MOR, which is based on word embeddings,
could be considered black box variables themselves and future work might
replace them by more transparent factors. One might also explore the
collinearity of the different regressors and define regressors more plausbibly
(especially MOR, which concurrently captures both semantic and morphological
aspects).

#### Peturbation Checklists

Sai et al. (2021) start out with the premise that evaluation in text
generation is a multi-faceted problem that cannot be captured, in general, by
one overall score. Instead, they propose perturbation checklists that evaluate
how susceptible metrics are towards predefined types of permutations as
evaluation criterion. This allows developers to check whether all invariances
that are required for a specific task are fulfilled. Since the goal is then to
evaluate a whole metric using perturbation templates, we classify it as a
global technique. We point out, however, that their approach could also
explain individual instances, just like the approach of Kaster, Zhao, and Eger
(2021).

To evaluate each metric with respect to different properties, they follow
Ribeiro et al. (2020b) and check how a metric behaves when property specific
changes are applied to the input. In particular, they compare the change in
the metric’s score with the change in score a human would assign after the
perturbation:

$\displaystyle
s(m)=\bigl{(}h(\hat{p})-h(p)\bigr{)}-\bigl{(}f_{m}(\hat{p})-f_{m}(p)\bigr{)}$
(1)

Here $s$ denotes the score for a perturbation template $t$ that applies
criterion (property) $c$. $m$ denotes the metric that is explained, $h$ is the
human score and $f_{m}$ the score achieved by $m$. Finally, $p$ is the
original model output and $\hat{p}$ the output after permutation. They
annotate $h$ using 15 annotators that determine, on a scale from 0 to 10, how
much a score should change for a perturbation. In total, they provide 34
perturbation templates. For MT, these templates encompass dropping or adding
of context as well as negations. For other tasks, they have other templates,
such as ones involving fluency or correctness (e.g., with respect to gender).
They report the following key findings:

  * •

BERTScore, BLEURT and Moverscore are shown to have problems to correctly
predict the score with antonyms and negations. This may not be surprising
given that BERT representations may be similar for antonyms.

  * •

The perturbation checklists allow to pick metrics that are strong with respect
to specific properties. E.g., Moverscore would capture fluency better than
BERTScore due to its ability to cope with the jumbling of words.

  * •

Overall, across metrics and NLG tasks, they show that existing evaluation
metrics are not robust against many simple perturbations and disagree with
scores assigned by humans. This allows for more fine-grained assessment of
automatic evaluation metrics which exposes their limitations and supports the
design of better metrics.

Following the definitions in Section 3, the perturbation checklists are model-
agnostic and advance all three goals: they can detect biases and model
shortcomings by applying respective perturbation templates; further, they
might improve accessibility in terms of improving the understanding what a
metric does. However, as it is a global technique, it does not support users
in understanding single decisions.

Limitations: A drawback of the approach is the need to craft specific
templates and the associated human annotation effort. Automatizing this
process would be highly desirable, cf. our novel approaches in Section 7.
Another problem in Eq. (1) above is that metrics may have different scales,
i.e., a metric may have a very narrow range and thus small deviations in
general. This may yield misleading conclusions when compared to the human
scale of annotations (the paper addresses this by normalizing all scores).
Varying scales are also one reason to choose preferences over Likert scales in
annotation tasks (Kiritchenko and Mohammad 2017).

##  7 New explainability approaches for MT evaluation

In this section, we present new results and techniques and discuss their
implications on explainable MT evaluation. First, in Section 7.1, we analyze
whether adversarial attacks can automatically spot weak spots in evaluation
metrics, thereby contributing to their explainability especially from a system
developer perspective. Then, in Section 7.2, we discuss novel simple
explainable metrics for the Eval4NLP shared task and show that they can
achieve strong results, which confirm the issue that the Shared Task’s setup
does not test the faithfulness of explanations. Finally, in Section 7.3, we
analyze system comparisons for the Eval4NLP shared task. This is an important
issue, as it contributes to the evaluation of explainability approaches for MT
metrics, a currently neglected research area.

###  7.1 Adversarial Attacks

Szegedy et al. (2014); Goodfellow, Shlens, and Szegedy (2015) show that neural
networks are susceptible to _adversarial attacks_ – minimal label-preserving
perturbations of the input of deep learning models that may change their
output. In object recognition for example, inputs can be augmented with noise
that is imperceivable for humans but leads to different prediction outcomes
(Szegedy et al. 2014; Goodfellow, Shlens, and Szegedy 2015). The NLP community
has differentiated between sentence-, word- and character-level attacks (Zeng
et al. 2021). Sentence- and word-level attacks often have the goal to produce
examples that are similar in terms of meaning (Alzantot et al. 2018; Li et al.
2020), while character-level attacks mimick various forms of typographical
errors (including visual and phonetic modifications) (Ebrahimi et al. 2018;
Pruthi, Dhingra, and Lipton 2019; Eger et al. 2019; Eger and Benz 2020;
Keller, Mackensen, and Eger 2021).

Interestingly, there is a deep connection between explainability and
adversarial attacks, which is underrecognized in the literature. For example,
Linardatos, Papastefanopoulos, and Kotsiantis (2021) list adversarial attacks
as tools for sensitivity analysis, one branch of methods they identify to
explain black box models. Gradient-based explanation techniques identify
salient words, while adversarial attacks target such vulnerable words (Li et
al. 2020; Jin et al. 2020, e.g.). A controversly discussed topic is the close
relation of adversarial examples and counterfactuals (Freiesleben 2021). Also,
presenting adversarial examples for a particular input could be interpreted as
a local explanation-by-example technique. Finally, knowing a model’s failure
cases – as adversarial attacks reveal – helps us better understand the model.
As such, we interpret our below adversarial attack experiments on evaluation
metrics as contributing to their explainability. Sai et al. (2021) and Kaster,
Zhao, and Eger (2021) follow similar approaches in that they perform input
perturbations from a manually selected range of options (e.g. negation,
replacing named entities, …). Our approach differs from the last two by
instead leveraging adversarial methods such as BERT-Attack (Li et al. 2020) to
_automatically_ find failure cases. This could evaluate robustness and help
understand model performance at a larger scale.

Figure 3: Schematic illustration of Adversarial Attacks on Machine Translation
Evaluation Metrics.

A generic setup of adversarial attacks on evaluation metrics is shown in
Figure 3. Here, an adversarial attacker takes the original input of a metric
and perturbs it to generate an adversarial hypothesis HYP∗. The metric then
computes another score for the adversarial example. The attack is successful
if this adversarial score is as much different from the original score as
possible and the perturbed hypothesis is as much similar to the original
hypothesis as possible. We note that the challenge lies in finding hypotheses
HYP∗ that minimally deviate from the original hypothesis while maximizing the
score differences.

Most word- and sentence- level adversarial attacks consider a classification
setting. To apply them to continuous metrics, we discretize the metric score
into classes following a three step process. First, we select a dataset and
calculate a metric score for each sample and metric. Second, we determine $k$
quantiles for each metric’s scores, where $k$ is the number of classes we want
to discretize to. Third, for each class of interest, we filter the metric
scores for samples that lie in the same class for all metrics. Class
probabilities are based on the procentual distance to the center of the class
intervals the metric score lies in between. We apply the following adversarial
attackers to the discretized scores:

  * •

BERT-Attack (Li et al. 2020) leverages BERT (Devlin et al. 2018) for word
replacement in a word-level adversarial attack. First, it ranks tokens based
on their importance for the original prediction (here the metric score) using
feature importance explanations. Then the tokens are iterated in order of this
ranking. For each token, $k$ replacement candidates are checked and if they
are not successful in keeping the original prediction and a similarity
constraint, the next token in the importance ranking is assessed. If the token
is a word, it is replaced with one of the top-$k$ replacement candidates
obtained by feeding the sentence to BERT (without usage of a mask). If the
token is a sub-word, the other sub-word tokens that belong to the word are
identified, all possible sub-word combinations are listed and their
probabilities to occur as a word replacement are determined with BERT. Each
replacement is evaluated with respect to the goal of the attack. Once the
original prediction changes to another quantile, the attack is successful.
When many sub-words occur in one word, the number of combinations can grow
prohibitively large. In these cases, we cap computation at 500k combinations.

  * •

Textfooler (Jin et al. 2020) also ranks the words by their feature importance.
The ranked words are iterated and, for each word, $k$ replacement candidates
are tested. The candidates are determined as the top-$k$ words that have the
same POS-tag and whose static word embeddings have a high cosine similarity to
the original word. Further, only candidates that keep the universal sentence
encoder (USE) (Cer et al. 2018) similarity to the original sentence higher
than a threshold are considered. If the predicted class does not change after
checking all candidates for a word, the candidate that minimized the predicted
class probability is kept and the next important word is perturbed.

  * •

Textfooler-Adjusted (Morris et al. 2020a) adds further constraints to
Textfooler. It sets higher thresholds on minimal word- and sentence-level
similarity (based on static word embeddings and USE). Additionally, it employs
an open-source grammar checker (Naber 2003) to impose a constraint on the
number of grammatical errors introduced by an attack.

We use TextAttack (Morris et al. 2020b) as framework for adversarial attacks.
The described attacks are originally _untargeted_ , i.e. they follow the goal
of changing the resulting class no matter the direction. However, we use a
targeted setup (with a desired target class specified) for BERT-Attack,
Textooler and Textfooler-Adjusted, by leveraging the respective class of
Textattack.

#### Automatic Evaluation

We apply attacks to a subset of the de-en (German-English) dataset of WMT19.

We divide the scores into 3 classes and filter for all samples that each
considered metric places in the same class. Improving translations is a
difficult task compared to making them worse, hence we investigate the change
from the highest class to the lowest class. There are 440 instances which fall
in the highest class for all metrics.666We consider a mix of hard and soft
reference-based as well as reference-free metrics.

Figure 4 shows the success rate of the attacks per metric, i.e., the
percentage of the time that an attack can find a successful adversarial
example by perturbing a given input. A surprising finding is that BERT-Attack
and Textfooler apparently perform very well against supervised metrics, have a
smaller success rate on metrics based on word-level similarities such as
XMoverscore and BERTScore and are least successful for hard metrics. This
result is to some degree counter-intuitive, as hard metrics could apparently
be fooled by simple lexical variations. The pattern is different for
TextFooler-Adjusted, which has low success rates throughout, except for
Transquest, where it has 22% success rate (followed by hard metrics).

In Figure 5, we further show the respective perturbation rates, the number of
new grammatical errors introduced, and the sentence similarity before and
after perturbation. These statistics are defined in Table 7. The perturbation
rates show that the BERT-Attack and TextFooler need fewer perturbations to
attack the supervised metrics, more perturbations for soft matching metrics
like XMoverScore and MoverScore and the most perturbations for hard metrics.
The pattern for TextFooler-Adjusted is again slightly different. BERT-Attack
on average introduces more grammatical errors than TextFooler, and TextFooler-
Adjusted makes fewest grammatical errors. TextFooler-Adjusted also produces
most similar hypotheses (measured via sentence similarity) and TextFooler
produces least similar hypotheses. To sum up, according to our automatic
evaluation, BERT-Attack and TextFooler are highly successful in attacking
supervised metrics, but they introduce more grammar errors and are less
faithful to the original hypotheses than TextFooler-Adjusted. The latter has
low success rates.

Success Rate |  Rate of successfully attacked samples over all samples (e.g. Tsai, Yang, and Chen 2019).
---|---
Perturbation Rate |  Average number of perturbed words over all words per sentence. (e.g. Morris et al. 2020b).
Rate of introduces gram. Errors |  Average number of new grammatical errors introduced per sample, as measured by Language Tool (Naber 2003; Morris et al. 2020a)
Sentence Similarity |  Average sentence similarity between original and perturbed sample using the SBERT toolkit (Reimers and Gurevych 2019).
Table 7: Quantitative metrics to measure the quality of adversarial attacks.
Figure 4: Success rate of adversarial attacks on 440 de-en samples from WMT19.

Figure 5: Top: Perturbation rate of adversarial attacks on 440 de-en samples
from WMT19. Middle: Avg. grammatical error introduced by adversarial attacks
on 440 de-en samples from WMT19. Bottom: Average sentence similarity of the
original and perturbed hypothesis on 440 de-en samples from WMT19. The black
tiles mean that no successful sample was found by the attack model for the
corresponding metric.

#### Human Evaluation

To investigate the validity of our above results, we also evaluate using
humans. To do so, we first split the successful attack samples into attacks to
reference-based metrics and attacks to reference-free metrics. Afterwards, we
sort both by sentence similarity as the first key and the number of introduced
grammatical errors as the second key (i.e. two of the indicators used for
metric robustness in the last paragraph). To check how effective this ordering
is to identify how meaning-preserving a sample is, we randomly select samples
from the top 10% (best) and worst 10% (worst) for the human evaluation (below,
we refer to this as pre-selection). Three co-authors of this work annotated 40
common instances of attacks, and each one of them annotated another 40
individual instances (one annotated 80 individual instances). Two of those
three co-authors (which were bilingual) in addition annotated 40 instances in
the reference-free scenario. In total, we thus have annotated 240 instances.

Figure 6 shows the distribution of the 240 annotated instances across the
dimensions (a) Metric and (b) Attack Model. Concerning Metric, BLEURT is the
most frequent metric appearing in our samples, METEOR the least frequent.
Concerning Attack Model, there are fewest examples from Textfooler-Adjusted,
as it has lowest success rate. Further, we note that we annotated 115 of the
best and 125 of the worst samples according to the ordering.

The annotation scheme was whether an attacked sentence preserved the adequacy
of the translation (label $=$ 0), made it worse (label $=$ 1) or considerably
worse (label $=$ 2). Prior to annotation, there was no communication among
annotators, e.g., no guidelines were established or how to deal with
particular cases.

Selected examples are shown in Table 8. Table 9 shows Cohen’s kappa between 3
annotators on the common set of annotated instances, Cohen’s kappa for a set
of annotations conducted twice by one of the annotators and statistics on the
distribution of annotations. Inter-annotator agreement is low, with kappa
slightly above 0.3. However, when only the decision ‘same’ (label $=$ 0) vs.
‘different’ (label $=$ 1,2) is made, the agreement is acceptable among all
annotators (0.64). A reason may be that it is often difficult to decide
whether a sentence in which only one or two words are changed (as the attacks
typically do) would count as ‘lower’ or ‘much lower’ adequacy. This may be
especially difficult to judge when one (often crucial) word is changed in a
long sentence.

Out of 40 common examples, only one is labeled as ‘same’ by all annotators;
each annotator individually only labels 3 out 40 of instances as ‘same’.
Across all samples, the mean of annotation labels is about 1.2 out of 2. On
average across all annotators, about 9% of adversarial attacks are annotated
as preserving the adequacy of the original hypothesis. Interestingly, the
supervised metric BLEURT is most frequently involved in these situations (it
also occurs most frequently in our data), followed by the hard metrics
SACREBLEU and SENTCHR (which occur much less frequently).

REF | It was a huge step in a cool season.
---|---
HYP | ”This was a huge step for a cool season.
HYP* |  ”This was a massive step for a cool season.
SRC | ”Dafür ist es einfach zu früh”, sagt Axel Büring.
HYP | ”It is simply too early for that,” says Axel Büring.
HYP* |  ”It is simply too early for that,” states Axel Büring.
REF | Many participants trained several hours a day.
HYP | Many participants practiced several hours a day.
HYP* |  Many participants practiced several calendar a dag.
Table 8: Exemplary human grades for sentences from WMT19. Top two cases: All annotators labeled ‘same’ (involved metric: SentCHR and XMoverScore, respectively). Bottom: All annotators labeled ‘much worse’. Kappa, inter, reference-based (3 Annotators, 40 Samples), fine | 0.369, 0.304, 0.354
---|---
Kappa, inter, reference-based (3 Annotators, 40 Samples), coarse | 0.639, 0.639, 0.639
Kappa, inter, reference-free (2 Annotators, 40 Samples), fine | 0.323
Kappa, inter, reference-free (2 Annotators, 40 Samples), coarse | 0.642
Kappa, intra, reference-based (1 Annotator, 40 Samples), fine | 0.616
Kappa, intra, reference-based (1 Annotator, 40 Samples), coarse | 0.875
Mean human class | 1.25
Avg. Proportion labeled ‘same’ | 9.01%
Table 9: Results of the human evaluation, inter- and intra-annotator kappa
agreement and average annotation.

Figure 7 shows that TextFooler-Adjusted beats the other two attacks in human
evaluation. For this reason, there are also fewer successes with this attack
model. Our evaluation shows that the permutations of Bert-Attack and
Textfooler not only introduce small errors, but often change the meaning of a
hypothesis completely. Typical cases include changes of referents/entities
(“In Spain” vs. “Across Castellano”), a complete destruction of the sense of
sentences (“It is simply too early for that” vs. “It is simply too after for
that”) or the introduction of small (grammar) errors (“After the dissolution
of the band” vs. “into the dissolution of the band”). Figure 8 shows that pre-
selecting based on semantic similarity of another model can improve the attack
quality. This is reasonable, as it can be seen as filtering for fulfilling a
constraint after an attack has been applied. Figure 9 shows how reasonable the
attacks on different metrics were according to the annotators. We can see that
SacreBLEU (SentenceBleu) could be fooled with better attacks than other
metrics; conversely, when MoverScore is fooled, the adequacy of the attacked
hypothesis is lowest, indicating that the metric reacted adequately to the
attack.

(a) Samples per Metric

(b) Samples per Attack

Figure 6: Sample distribution per metric and per attack. Figure 7: Mean per
attack model. We assign the human labels to “Same”=0, “”Worse”=1 and “Much
Worse”=2. Figure 8: Mean per pre-selection. We assign the human labels to
“Same”=0, “”Worse”=1 and “Much Worse”=2. Figure 9: Mean per metric. We assign
the human labels to “Same”=0, “”Worse”=1 and “Much Worse”=2.

We conclude that automatic adversarial attacks on evaluation metrics are
apparently not feasible using the selected established adversarial attack
models. While tools like BERT-Attack and TextFooler may to some degree be
useful for simpler text classification tasks, we find that less than 10% of
attacks on evaluation metrics are legitimate – i.e., label-preserving – in our
human assessment (more restrictive tools such as TextFooler-Adjusted are more
suited, but have much lower success rates). A reason may be that evaluation
metrics may be considered a harder task than classification; for example,
changing an entity in a translation (“Bill is rich” vs. “Tom is rich”) may not
alter its sentiment, subjectivity, etc., but certainly its adequacy. In other
words, for MT evaluation, the notion of label-preserving coincides with the
notion of meaning-preserving. To preserve meaning seems beyond the scope of
many current adversarial techniques, however. We also point out the simple
design scheme of many current adversarial attack models, which aim to keep the
sentence structure but only rewrite one or two important words. More clever
adversarial attacks implemented via humans, such as retaining the semantics
but aggressively changing the surface level of the hypothesis (Kaster, Zhao,
and Eger 2021) or carefully designed check lists (Sai et al. 2021), can still
possibly expose important weak spots of metrics.

###  7.2 Hard Reference Free Metrics for the Eval4NLP Shared Task

#### Hard Reference Free Metrics

We provide results for four additional pairs of metric + explainability
techniques, which we will refer to as hard reference-free metrics. Hard
metrics like BLEU and METEOR are interpretable (which makes them attractive
for the Shared Task) and reference-based. In order to explore how well the
word-level scores extracted from these metrics perform, we obtain pseudo
references using (1) M2M100 (Fan et al. 2020)777We use the library EasyNMT by
Nils Reimers, https://github.com/UKPLab/EasyNMT and (2) word by word (wbw)
translations using a static dictionary, obtained with Google
Translate.888https://translate.google.com/ Further, we use SHAP (Lundberg and
Lee 2017) to extract word-level scores from BLEU and METEOR.999As BLEU and
METEOR are interpretable, word-level scores could also be computed from them
directly. We leave this to future work.

Table 10 shows the results on the test sets compared with the baselines of the
Shared Task101010In contrast to the setting of the shared task, we only
present the word-level explanation of the hypothesis, as the explanation of
the source cannot immediately be retrieved from the setup. An explanation of
the source might be retrieved by translating the hypothesis into the source
language and applying the hard metric there.. The results are unexpected, as
the hard reference-free metrics outperform the baselines on the whole
concerning word-level explanation plausibility. Even with word-by-word
translations, they often outperform the Shared Task baselines. On average
across all languages, the translated METEOR with SHAP beats Transquest with
LIME by $0.21$ difference in AUC, $0.11$ difference in AP and $0.1$ difference
in RtopK.111111In the Shared Task, the explanations achieve rank $6.67$ for
et-en, $6.33$ for ro-en, $4.67$ for ru-de and rank $7$ de-zh. These findings
suggest that due to the interpretability of METEOR and BLEU, SHAP is able to
extract better feature importance scores than from e.g. XMoverScore. This is a
counterintuitive finding, as BLEU and METEOR have been show to be inferior to
soft metrics on a sentence-level. A possible reason is that the explainability
techniques of the baselines have difficulties to explain the more complex
models. This shows, however, that there is not always a relation between
explanation and model output, a property that is ignored when only evaluating
for plausibility. Hence, an additional evaluation for faithfulness (see
Section 3.4) could prove beneficial for evaluations with the goal of increased
model understanding. In Table 10, we also do not observe a correspondence
between sentence- and word-level scores; the latter are better for our novel
baselines, but the sentence-level scores are substantially higher for the
trained Transquest metric for the language pairs involving English (on which
the metrics have been trained), further highlighting the need for faithfulness
evaluation.

|  | Hypothesis |
---|---|---|---
Lang. | System | AUC | AP | RtopK | Pearson
et-en |  TranslationBLEU${}_{\text{full}}$ \+ SHAP | 0.754 | 0.541 | 0.418 | 0.482
|  TranslationMETEOR${}_{\text{full}}$ \+ SHAP | 0.754 | 0.562 | 0.444 | 0.581
|  TranslationMETEOR${}_{\text{wbw}}$ \+ SHAP | 0.670 | 0.445 | 0.323 | 0.318
| Transquest+LIME (Baseline) | 0.62 | 0.54 | 0.43 | 0.77
ro-en |  TranslationBLEU${}_{\text{full}}$ \+ SHAP | 0.831 | 0.592 | 0.446 | 0.562
|  TranslationMETEOR${}_{\text{full}}$ \+ SHAP | 0.826 | 0.603 | 0.458 | 0.701
|  TranslationMETEOR${}_{\text{wbw}}$ \+ SHAP | 0.752 | 0.493 | 0.342 | 0.397
| Transquest+LIME (Baseline) | 0.63 | 0.52 | 0.42 | 0.90
ru-de |  TranslationBLEU${}_{\text{full}}$ \+ SHAP | 0.795 | 0.525 | 0.420 | 0.490
|  TranslationMETEOR${}_{\text{full}}$ \+ SHAP | 0.806 | 0.546 | 0.435 | 0.597
|  TranslationMETEOR${}_{\text{wbw}}$ \+ SHAP | 0.679 | 0.399 | 0.288 | 0.273
| Transquest+LIME (Baseline) | 0.40 | 0.26 | 0.16 | 0.50
de-zh |  TranslationBLEU${}_{\text{full}}$ \+ SHAP | 0.633 | 0.391 | 0.261 | 0.275
|  TranslationMETEOR${}_{\text{full}}$ \+ SHAP | 0.647 | 0.420 | 0.294 | 0.376
|  TranslationMETEOR${}_{\text{wbw}}$ \+ SHAP | 0.617 | 0.390 | 0.273 | 0.222
| Transquest+LIME (Baseline) | 0.46 | 0.27 | 0.14 | 0.34
Table 10: Performance of “hard reference-free metrics” on the Eval4NLP shared
task. Metric${}_{\text{full}}$ indicates that a full translated sentence was
used as pseudo-reference. Metric${}_{\text{wbw}}$ indicates that the word by
word translated source was used as pseudo-reference. Bold values indicate the
best score per column and language pair.

###  7.3 Rigorous system comparison

In the Eval4NLP shared task, systems are ranked according to their global
independent statistics, e.g., mean AUC scores of different systems over a
common set of test instances. However, aggregation mechanisms such as the mean
ignores which system beats others over individual instances, and thus may lead
to false conclusions. For instance, Peyrard et al. (2021) illustrate that a
system that is worse than another on all instances but one (an outlier) might
be falsely declared the winner according to _independent_ statistics such as
the mean or median. As remedy, they suggest to use the BT (Bradley-Terry)
model (Bradley and Terry 1952), which performs _paired_ evaluation, to conduct
rigorous comparison for competing systems. BT leverages instance-level pairing
of metric scores from different systems121212BT is a statistical model used to
maximize the likelihood of the given number of instances where one system is
better than another, aiming to estimate unknown, inherent strengths of
systems. In the case of two systems, the strength of system A is equivalent to
the percent of instances of A better than system B., and assumes that a
winning system should beat others over the majority of instances. In the
concrete case – the shared task – this would mean that a system could have
very high AUC scores on few instances, which inflate its mean AUC, but
otherwise performs worse in the majority of instances.

| de-zh | ro-en | et-en | ru-de
---|---|---|---|---
source.ap-scores | $9\%$ | $14\%$ | $7\%$ | $3\%$
source.auc-scores | $3\%$ | $7\%$ | $9\%$ | $0\%$
source.rec-topk-scores | $38\%$ | $20\%$ | $23\%$ | $14\%$
target.ap-scores | $4\%$ | $10\%$ | $2\%$ | $2\%$
target.auc-scores | $2\%$ | $8\%$ | $4\%$ | $0\%$
target.rec-topk-scores | $31\%$ | $19\%$ | $13\%$ | $13\%$
Table 11: Disagreement of system rankings between mean and BT across six
evaluation metrics and four language pairs. Each cell shows the percent of
system pairs ordered differently by mean and BT according to the recalled
version of Kendall’s $\tau$ supported on $[0,1]$. Higher scores indicate
higher disagreement.

Figure 10: Results of pairwise comparison according to (top) “source.rec-topk-
scores” and (bottom) “target.rec-topk-scores” over system pairs for German-
Chinese. Each cell denotes the percent of the instances in which one system
(in rows) beats another (in columns). We mark cells for which system pairs
have significant differences according to Sign test with ‘*’. Systems have
been ranked reversely by BT, e.g., systems in final rows are declared the
best. mean declares Kyoto-1 as the best in both (top) and (bottom) settings.

We analyze whether mean and BT yield similar results on the shared task.
First, we quantify the disagreement between mean and BT. Table 11 shows that
mean and BT often disagree on the ranking of systems, especially for
“source.rec-topk-scores” and “target.rec-topk-scores”. This might undermine
the reliability of these recall-based metrics, as they are very sensitive to
the aggregation scheme (BT vs. mean), unlike ‘ap-scores’ and ‘auc-scores’ that
consider both precision and recall.

We then provide justifications to understand the judgments of BT and mean on
German-Chinese as use case (see Figure 10). We find that BT and mean both may
yield wrong judgments as to which system is the state-of-the-art. We
illustrate this below:

  * •

mean might be wrong (Fig. 10, top): Considering plausibility of explanations
on source sentences, mean declares Kyoto-1 as the best system; however, it
significantly outperforms merely 3 out of 9 systems according to pairwise
comparison. This indicates that MEAN results are very likely wrong. In
contrast, BT chooses Unbabel-18 according to that it wins in 8 out of 9 cases.

  * •

BT might be wrong (Fig. 10, bottom): Considering plausibility of explanations
on target sentences, BT declares Unbabel-18 as the best, as it beats 7 out of
10 systems with clear wins. On the other hand, Kyoto-1 (ranked 5th according
to BT) wins in 9 out of 10 systems, and it also beats Unbabel-18. This means
Kyoto-1 might be the winner, but that BT nevertheless favors Unbabel-18 most
as BT considers the number of instances of one system superior to another.
Concretely, though Kyoto-1 beats the greatest number of systems, it
outperforms these systems on slightly over half of instances, which reflects
the weak strength from a BT perspective. In contrast, Unbabel-18 wins globally
on the greatest number of instance-level pairs assembled across all systems.
We depict this issue of BT as the inconsistency between global and local
judgments, i.e., that one locally beats another in the case of two systems,
but the judgment of system superiority may change in the global view when
involving more systems in the comparison. As Peyrard et al. (2021) state, the
‘inconsistency’ can hardly be addressed according to the Arrow’s impossibility
theorem Arrow (1950).

Our analysis shows how subtle the evaluation of systems which (in the case of
the shared task) explain MT metrics can be and that there may be no clearly
best explainability model, as none of the systems beats all other systems
according to pairwise comparison. We recommend future such evaluations to
consider multiple aggregation schemes (including mean and BT) for a more fine-
grained assessment.

##  8 Future Work

In this section, we lay out ideas for future explainability approaches of MT
metrics.

###  8.1 Text generation as explainability for text generation metrics

Providing explanations in text form (Camburu et al. 2018; Kumar and Talukdar
2020) may be particularly attractive to human users. For MT systems, this
would mean that a metric not only outputs one or several scores, but also
generates a textual explanation for the metric score. Below, we provide a
vision of concrete text generation approaches as explanation for MT but we
also point out that we could even more radically envision a new class of
holistic generalized MT systems that output translations, scores, and human-
understandable explanations.

#### Inverse Metrics

Adversarial attacks produce examples that lie as close to the original input
as possible. The property which makes them interesting for local explanations
is that the change can still be perceived, which yields insights for the user
or developer.

However, an explanation does not necessarily have to be close to the original
sentence. To that end, we introduce inverse metrics, which can be interpreted
as a special form of adversarial attacks or counterfactual examples.131313A
similar approach can be found in Computer Vision, where CNNs are explained by
inverting their computation (Mahendran and Vedaldi 2014). We define an inverse
metric METRIC-1 as follows:

$\displaystyle\text{METRIC}^{-1}(\text{s},\text{score})=\text{HYP*}\>\iff\>\text{METRIC}(\text{
s},\text{HYP*})=\text{score}$ (2)

where $s$ is the source or reference.

In other words, the inverse metric returns a hypothesis for which the metric
will return a given score. For example, if a good reference-free machine
translation evaluation metric assigns a score of 1 to a perfect translation of
a source, the inverse metric of the source and the score 1 should ideally
return a perfect translation as well. If the lowest possible score is 0, the
inverse metric of the source and the score 0 should return a translation that
is as irrelevant as possible (as measured by the metric). Note that the output
of an inverse metric could be interpreted as a targeted, unconstrained
adversarial attack on metrics with numeric outputs, or, depending on the
definition, as a counterfactual example. When the target score is equal or
similar to the original score, the output can be viewed as a prototypical
example for the score.

Figure 11 gives a hypothetical example of how such examples can provide
insights into a model’s local behavior. By sampling other hypotheses around
the original score, it could be shown what the metric indicates as better or
worse than the original hypothesis. In case of metrics with a good
performance, this could be used to improve translations. In case of weak
metrics, it could show the failure cases of the metric, e.g. if the inverse
metric finds hypotheses that are grammatically worse but get higher scores.

Figure 11: Inverse Metrics: Hypothetical examples around a given hypothesis.

To implement inverse metrics, we used a technique that iteratively perturbs
words based on mask replacement, to find an input that results in a score as
close to the desired target score as possible (see Appendix A for details).
Table 12 shows example neighborhoods we queried for BLEU (Papineni et al.
2002) and BLEURT (Sellam, Das, and Parikh 2020) (we use BLEURT-base-128). I.e.
we use an interpretable metric and a black box metric. Based on these results,
we can verify that BLEU does not try to preserve semantics as an unrelated
(lexical overlap) sentence such as “My cat is at home” receives a better score
than the original hypothesis. Further, for BLEURT we acquire a rather
interesting improvement “My cat dates back two generations .”. While close in
meaning, this is not a phrase that would be used in natural language. As the
technique produces a neighborhood for a sample, it might also be integrated
into other explainability techniques which require neighborhood examples for
their computation (e.g. LIME).

REF | My cat is old. |
---|---|---
HYP | My cat lives since 17 years. |
BLEU |  | Orig. Score: 0.253
HYP* | The boy lives since then ? | Pert. Score: 0.0
HYP* | My family lives since then ? | Pert. Score: 0.193
HYP* | My cat is on fire ? | Pert. Score: 0.398
HYP* | My cat is on 17 . | Pert. Score: 0.427
HYP* | My cat lives since 17 . | Pert. Score: 0.302
HYP* | My cat is at home . | Pert. Score: 0.427
BLEURT |  | Orig. Score: -0.514
HYP* | This cat survived about three years . | Pert. Score: -0.999
HYP* | My cat lived for 17 years . | Pert. Score: -0.594
HYP* | My cat has aged ten years . | Pert. Score: -0.111
HYP* | My cat dates back two generations . | Pert. Score: 0.115
HYP* | My cat lives since 17 years . | Pert. Score -0.514
HYP* | My cat has survived many years . | Pert. Score -0.184
Table 12: Inverse metrics: exemplary hypothesis neighbourhood generated with
the algorithm in Appendix A. From top to bottom, the target scores were chosen
to be $[0,0.2,0.4,0.6,0.8,0.9]$ for BLEU and $[-1,-0.6,-0.2,0.2,0.6,0.8]$ for
BLEURT.

###  8.2 Explainability for Discourse Metrics

As text generation systems become better and better, more and more MT systems
will expectedly operate on document-level in the future (Voita, Sennrich, and
Titov 2019), rather than on sentence-level, as is the current standard. The
corresponding evaluation metrics will need to be able to take sentence-level
context into account as well. This is an emergent topic in NLG evaluation, see
e.g. Jiang et al. (2021); Zhao, Strube, and Eger (2022). For instance, Joty et
al. (2017) proposed DISCOTK to address discourse coherence evaluation, which
compares hypothesis with reference on the level of rhetorical structures.
Jiang et al. (2021) presented BLOND, which measures the consistency of gender
and verb tense between hypothesis and reference. Zhao, Strube, and Eger (2022)
introduced DiscoScore, which compares readers’ focus of attention in
hypothesis with that in reference. Among the three, BLOND and DISCOTK are
transparent metrics, both of which adopt simple statistics to measure the
inconsistency on the levels of verb tense, gender and structures. To achieve
higher quality, DiscoScore is based on blackbox language models, which makes
it non-transparent. Even though Zhao, Strube, and Eger (2022) provided
justifications to the superiority of DiscoScore over other metrics, little is
known how much the ‘blackbox’ judgments are trustworthy—which is one of the
major goals in explainable artificial intelligence Arrieta et al. (2020), and
also in this work.

As for explainable high-quality document-level evaluation metrics, a range of
post-hoc techniques could play a vital role for understanding non-transparent
discourse metrics as extensions of what has been surveyed in this work for
sentence-level metrics, e.g., providing rationales to the judgments of these
metrics in the form of (i) importance distribution, viz., the probability
distribution of words in hypothesis that exhibit discourse errors; (ii)
simpler, transparent models such as linear regression and decision trees;
(iii) generated textual explanations, etc.

The challenge of such extensions lies in the additional complexity of
explainable document-level metrics. For example, error annotations would also
need to highlight cross-sentence relations and account for divergent
linguistic structure. Also, transparent surrogate models (e.g., linear
regressions) explaining the blackbox ones would need to be able to take cross-
sentential context into account, which involves cross-lingual discourse
phenomena such as “coreference resolutions in source and hypothesis”.
Adversarial examples on the document-level (e.g., wrong gender agreement,
wrong reference relations) would be particularly insightful for the
development of better document-level metrics.

###  8.3 Leveraging the interplay between word-level explanations and
sentence-level metrics

In the Eval4NLP shared task, explainability techniques were used to explain
sentence-level metrics scores by word-level scores, yielding a plausibility
evaluation. On the other hand, Freitag et al. (2021) and also a few Eval4NLP
shared task participants (e.g., Polák, Singh, and Bojar (2021)) show that
word-level scores may be used to infer sentence-level scores. This is an
interesting duality between word-level and sentence-level metrics, which
future work may exploit. A particular appeal lies in the fact that word-level
rationales may be extracted from sentence-level metrics in an unsupervised
manner using the explainability techniques, giving rise to self-supervised
improvement techniques (Belouadi and Eger 2022).

###  8.4 Extrinsic Evaluation of Explanations for MT Metrics

As discussed in Section 3.4, explanations could be evaluated intrinsically
(with respect to some desirable properties) and extrinsically (measured via
improved outcome on downstream tasks after incorporating the explanations).
Concerning intrinsic evaluation, we have seen the 2021 Eval4NLP Shared Task
(Fomicheva et al. 2021) focusing on evaluating plausibility of the
explanations, i.e., how sensible the relevance scores are when compared to
word-level errors. The evaluation of generated adversarial samples in Section
7.1 is another example of intrinsic evaluation. However, only few existing
works on explainability for MT metrics (e.g., Sai et al. (2021)) conduct
extrinsic evaluation. In other words, most related works do not check whether
their explanations can truly help achieve goals discussed in Section 4:
providing further information for non-experts and non-native speakers,
diagnosing and improving the metrics, increasing efficiency of annotators for
word-level translation errors, etc. Hence, it would be interesting for future
work to test these goals practically. For instance, one may develop an
annotation tool which shows explanations for MT metrics as supporting
information and measure human annotators’ efficiency, compared to the case
where they use the system with no explanation. Also, developing a new
framework for incorporating human feedback on different types of explanations
to improve the metric is another way to evaluate the explanations with respect
to a downstream task (i.e., metric improvement) (Lertvittayakumjorn and Toni
2021). Lastly, it is also possible to measure user trust in the metrics with
and without the explanations so as to assess whether the explanations can
boost the user trust and promote adoption of complex model-based metrics
(Hoffman et al. 2018; Jacovi et al. 2021).

###  8.5 Sentence-Pair Feature Importance

With GMASK, Chen et al. (2021) introduce an explainability technique that
provides hierarchical feature attributions on sentence pairs. To explain, the
technique forms groups of words between two input sentences and assigns each
group an importance score. This approach is highly relevant for MT metrics, as
these are based on sentence pairs in most cases. For example, when one word in
a source is translated into multiple words in the hypothesis, GMASK could
identify this connection and provide joint importance values. In particular,
the method seeks to fulfill three core properties: (1) providing correct
importance selections, (2) considering only the most relevant words, (3)
masking correlated words together. GMASK learns binary masks that indicate for
each embedding in the two input sentences whether it should be dropped or
kept. Thereby, it tries to keep the originally predicted output while reducing
the number of relevant words. Ideally, when used for explainable MT
evaluation, the approach might provide outputs like the hypothetical example
shown in table, where each color indicates a different group 13.

REF |  I have an apple, which is green .
---|---
HYP |  My apple is green .
Table 13: Hypothetical explanation with GMASK. The color indicate words that
are grouped together.

Chen et al. (2021) evaluate their approach on a dataset for natural language
inference (NLI) and three datasets for paraphrase identification. To evaluate
faithfulness, they calculate an AOPC score, post-hoc accuracy, and perform a
degradation test (see Section 3.4) to check how well the most-relevant and
least-relevant tokens predicted by GMASK influence the original outcome.
Overall, their results indicate that their method is the most faithful
compared to methods that target single sentence inputs.

GMASK has not yet been applied to evaluation metrics. A particular challenge
is that metrics give continuous scores, while GMASK has hitherto been applied
for classification tasks. Also, GMASK employs a preselection approach that is
problematic in case of MT metrics, as it will likely drop errors in favour of
correctly translated words. If these challenges are overcome we fathom that
this approach could provide strong explanations.

##  9 Conclusion

The difficulty of understanding machine learning models has implications on
their real world usage. For example, it is dangerous to employ black-box
systems in safety critical applications (Rudin 2019). Also, they might
unknowingly incorporate biases such as gender, racial, political or religious
bias (Mehrabi et al. 2021). In their general data protection regulation, the
European Union even requires that decisions that impact a person can be
explained (Goodman and Flaxman 2017). Hence, the interpretability and
explainability of machine learning models forms a gateway to their broader
usage. Further propelled by recent challenges, such as the eXplainable AI
challenge by Gunning (2016) or the explainable ML (xML) challenge by the Fico
(2018), a large body of research considers this problem.

In this work, we provide a taxonomy of goals and properties for _explainable
evaluation metrics_ , a nascent field that may help overcome the dominance of
classical low-quality evaluation metrics. We also synthesize and categorize
recent approaches to explainable evaluation metrics, highlighting their
results and limitations. Currently, the two dominant approaches to
explainability for evaluation metrics include (1) highlighting erroneous words
in source and target that explain a sentence-level score and (2) manually
devising adversarial attacks that expose weak spots of metrics and which can
then be used to diagnose and improve metrics. The major weaknesses of the
current realization of these techniques are that (1i) the error highlights do
not consider the correspondence between words in source and target and (1ii)
the severity of errors and (1iii) the evaluation does not consider the
faithfulness of explanations. Further, (2i) adversarial attacks to evaluation
metrics concurrently require human design and (2ii) automatizing this process
is very difficult as we show, since adversarial attacks need to be meaning-
preserving (which is harder than what current adversarial techniques aim for).
We also present a vision of future approaches to explainable evaluation
metrics, which should (A) help fix the problems of the above paradigms (e.g.,
via joint consideration of the sentence pairs involved), (B) go beyond the
named approaches and also consider textual explanations (which may be easier
to comprehend for humans), (C) leverage explainability techniques to
unsupervisedly improve sentence-level metrics, (D) target document-level
metrics (which exhibit an additional layer of complexity) and (E) provide
extrinsic evaluation across a range of different explanation types.

Our broader vision is that explainability is now a ‘desirable but optional’
feature, but we argue that in the future it will become essential, even
compulsory, especially for evaluation metrics as a highly sensitive task
assessing the quality (and veracity) of translated information content.
Explainability builds transparency and trust for users, eases bug-fixing and
shortens improvement cycles for metric designers, and will be required by
law/regulations for AI systems to be applied to large-scale, high-stack
domains. In this context, we hope our work will catalyze efforts on the topic
of explainable evaluation metrics for machine translation and more general
text generation tasks.

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