# A Survey of Code-switching: Linguistic and Social Perspectives for Language
Technologies

A. Seza Doğruöz
Ghent University, Gent, Belgium
<EMAIL_ADDRESS>
Sunayana Sitaram
Microsoft Research India, Bangalore, India
<EMAIL_ADDRESS>
Barbara E. Bullock
UT at Austin, Austin, USA
<EMAIL_ADDRESS>
Almeida Jacqueline Toribio
UT at Austin, Austin, USA
<EMAIL_ADDRESS>

###### Abstract

The analysis of data in which multiple languages are represented has gained
popularity among computational linguists in recent years. So far, much of this
research focuses mainly on the improvement of computational methods and
largely ignores linguistic and social aspects of C-S discussed across a wide
range of languages within the long-established literature in linguistics. To
fill this gap, we offer a survey of code-switching (C-S) covering the
literature in linguistics with a reflection on the key issues in language
technologies. From the linguistic perspective, we provide an overview of
structural and functional patterns of C-S focusing on the literature from
European and Indian contexts as highly multilingual areas. From the language
technologies perspective, we discuss how massive language models fail to
represent diverse C-S types due to lack of appropriate training data, lack of
robust evaluation benchmarks for C-S (across multilingual situations and types
of C-S) and lack of end-to-end systems that cover sociolinguistic aspects of
C-S as well. Our survey will be a step towards an outcome of mutual benefit
for computational scientists and linguists with a shared interest in
multilingualism and C-S.

##  1 Introduction

It is common for individuals in multilingual communities to switch between
languages in various ways, in speech and in writing. In example 1, a bilingual
child alternates between German and Turkish (in bold) to describe her teacher
at school. Note that the Turkish possessive case marker (_-si_) is attached to
a German noun Karakoç and Herkenrath (2019).

  1. 1.

Frau Kummer. Echte Name-si Christa.
Ms. Kummer. Real Name-Poss.3sg Christa.
‘Ms. Kummer. (Her) real name is Christa’

The goal of this paper is to inform researchers in computational linguistics
(CL) and language technologies about the linguistic and social aspects of
code-switching (C-S) found in multilingual contexts (e.g. Europe and India)
and how linguists describe and model them. Our intent is to increase clarity
and depth in computational investigations of C-S and to bridge the fields so
that they might be mutually reinforcing. It is our hope that interested
readers can profit from the insights provided by the studies reported in this
survey, for instance, in understanding the factors that guide C-S outcomes or
in making use of existing annotation schema across multilingual contexts.

##  2 Competing models of C-S

For linguists, the specific ways in which languages are switched matters. The
use of a single Spanish word in an English tweet (ex. 2) is not as
syntactically complicated as the integration in ex. 1. In fact, it may not
signal multilingualism at all, simply borrowing. Many words, particularly
anglicisms, circulate globally: _marketing, feedback, gay_.

  1. 2.

This is a good baile!
‘This is a good dance party!’ Solorio and Liu (2008)

To produce example (2), the speaker needs to know only one Spanish word. But,
to produce example (1), the speaker has to know what word order and case
marker to use, and which languages they should be drawn from. NLP scholars are
not always concerned with the difference between examples (1) and (2) so that,
with some exceptions Bhat et al. (2016), grammatical work in NLP tends to rely
heavily on the notion of a matrix language model advanced by Joshi (1982) and
later adapted by Myers-Scotton (1997) as the Matrix Language Frame (MLF)
model. The MLF holds that one language provides the grammatical frame into
which words or phrases from another are embedded and its scope of application
is a clause. Thus, it would not apply to the _alternational_ English-Afrikaans
C-S in example (3) as each clause is in a separate language Dulm (2007).

  1. 3.

I love Horlicks maar hierś niks
‘I love Horlicks but there’s nothing there ’

Although it dominates computational approaches to C-S, the MLF is contested on
empirical and theoretical grounds. The consistent identification of a matrix
language is not always possible, the criteria for defining it are ambiguous,
and its scope is limited Meakins (2012); Bhat et al. (2016); Adamou (2016);
MacSwan (2000); Auer and Muhamedova (2005). Bullock et al. (2018)
computationally show that different ways of determining the matrix language
only reliably converge over sentences with simple insertions as in example
(2).

For many linguists, the MLF is not the only way, or even an adequate way, to
theorize C-S. The Equivalence Constraint Poplack (1980) captures the fact that
C-S tends to occur at points where the linear structures of the contributing
languages coincide, as when the languages involved share word order. Other
syntactic theories are built on the differences between lexical and functional
elements, including the Government Constraint DiSciullo et al. (1986) and the
Functional Head Constraint Belazi et al. (1994). Incorporating the latter in
NLP experiments has been shown to improve the accuracy of computational and
speech models Li and Fung (2014); Bhat et al. (2016). Functional elements
include negative particles and auxiliaries, which are respectively classified
as Adverbs and Verbs (lexical classes), in some NLP tag sets AlGhamdi et al.
(2016). This means that NLP experiments often use annotations that are too
coarse to be linguistically informative with regard to C-S. Constraint-free
theories Mahootian and Santorini (1996); MacSwan (2000) hold that nothing
restricts switching apart from the grammatical requirements of the
contributing languages. Testing such theories in NLP experiments would require
syntactically parsed corpora that are rare for mixed language data Partanen et
al. (2018). In sum, working together, theoretical and computational linguists
could create better tools for processing C-S than those currently available.

##  3 Why do speakers code-switch?

In addition to focusing on the linguistic aspects and constraints on C-S,
linguists are also interested in the social and cognitive motivations for
switching across languages. What a (multilingual) speaker is trying to achieve
by switching languages can affect its structural outcome. Linguists recognize
that pragmatic, interactional, and socio-indexical functions may condition C-S
patterns. For instance, Myslín and Levy (2015) demonstrate that Czech-English
speakers switch to English for high-information content words in prominent
prosodic positions when speaking Czech. Other uses of C-S with structural
traces include signalling an in-group identity through _backflagging_ Muysken
(1995) or _emblematic_ tag-switching Poplack (1980). These are words or
phrases that are used at the edge of clauses (e.g., Spanish ojalá or English
so). Other functions, among these, quoting a speaker, getting the attention of
an interlocutor, or reiterating an utterance to soften or intensify a message
will also be indicated via C-S in predictable linguistic constructions, such
as with verbs of ‘saying’, vocative expressions, and sequential translation
equivalents Gumperz (1982); Zentella (1997).

According to Clyne (1991), there are eight factors (e.g. topic, type of
interaction, interlocutors, role relationship, communication channel) that can
influence C-S choices. Lavric (2007) explains C-S choices in line with
politeness theory, focusing on prestige and face-saving moves in multilingual
conversations. Heller (1992) takes a macro-social view, arguing that French-
English C-S in Quebec may signal a political choice among both dominant and
subordinate groups.

Gardner-Chloros and Edwards (2004) suggest that social factors influence
language choice, with different generations of speakers from the same
community exhibiting very different C-S patterns. Similarly Sebba (1998)
argues that as speakers cognitively construct equivalence between morphemes,
words, and phrases across their languages, communities of the same languages
may do this differently. Evidence from computational studies suggests that C-S
is speaker-dependent Vu et al. (2013). Gender and identity also play a role
for C-S practices in English and Greek Cypriot community in London Finnis
(2014). From a computational perspective, Papalexakis et al. (2014)
investigated the factors that influence C-S choices (Turkish-Dutch) in
computer mediated interaction and how to predict them automatically.

##  4 Code-switching, Borrowing, Transfer, Loan Translation

While C-S implies active alternation between grammatical systems, borrowing
does not. It is difficult to know if a lone word insertion (e.g. example (2))
constitutes a borrowing or a C-S without considering how the items are
integrated into the grammar of the receiving language Poplack et al. (1988).
When such analyses are done, most lone-item insertions are analyzable as one-
time borrowings, called _nonce borrowings_ Sankoff et al. (1990). Similarly,
what looks like complex C-S may not be perceived as switching at all. Auer
(1999) distinguishes a continuum of mixing types: prototypical C-S is
pragmatic and intentional, _Language Mixing_ serves no pragmatic purpose, and
_Mixed Languages_ are the single code of a community. These can look
structurally identical, but the latter can be modeled as a single language
(e.g. languages like Michif Cree Bakker (1997) or Gurinji Kriol Meakins
(2012)) rather than the intertwining of two. Bilaniuk (2004) describes the
Surzhyk spoken by urban Russian-Ukrainian bilinguals (in Ukraine) as ‘between
C-S and Mixed Language’ since speakers are highly bilingual and the direction
of switching is indeterminate.

Loan translation and transfer involve the words from only one language but the
semantics and grammatical constructions from the other. In example 4, the
Turkish verb _yapmak_ ,‘ to do’, takes on the Dutch meaning of _doen_ in
Turkish spoken in the Netherlands Doğruöz and Backus (2009).

  1. 4.

İlkokul-u İstanbul-da yap-tı-m.
primary.school-ACC İstanbul-LOC do-past-1sg.
‘I finished primary school in Istanbul.’

In transfer, grammatical constructions can be borrowed from one language to
another without the words being borrowed. Treffers-Daller (2012) demonstrates
the transfer of verb particles from Germanic languages into French. In
Brussels French (Belgium), the construction _chercher après_ ‘look after’ (for
‘look for’) is a translation of the Dutch equivalent and, in Ontario French
(Canada), _chercher pour_ is the translation equivalent of English ‘look for’.
In reference French (France), there is normally no particle following the
verb. The degree to which linguistic features like loan translation and
transfer can be found alongside C-S is unknown.

##  5 C-S across Languages: European Context

The contexts in which people acquire and use multiple languages in Europe are
diverse. Some acquire their languages simultaneously from birth, while others
acquire them sequentially, either naturally or via explicit instruction.
Multilingualism is the norm in many zones where local residents may speak
different languages to accommodate their interlocutors. Speakers who use local
dialects or minoritized varieties may also be engaged in C-S when switching
between their variety and a dominant one Mills and Washington (2015); Blom and
Gumperz (1972).

C-S in bilingual language acquisition of children has been studied across
language contact contexts in Europe. In Germany, Herkenrath (2012) and Pfaff
(1999) focused on Turkish-German C-S and Meisel (1994) on German-French C-S of
bilingual children. From a comparative perspective, Poeste et al. (2019)
analyzed C-S among bilingual, trilingual, and multilingual children growing up
in Spain and Germany. In the Netherlands, Bosma and Blom (2019) focused on C-S
among bilingual Frisian-Dutch children. In addition to analyzing C-S in
children’s speech, Juan-Garau and Perez-Vidal (2001) and Lanza (1998)
investigated C-S in the interaction patterns between bilingual children and
their parents (i.e. Spanish-Catalan and English-Norwegian respectively).

Within an educational setting, Kleeman (2012) observed C-S among bilingual
(North Sami-Norwegian) kindergarten children in the North of Norway.
Similarly, Jørgensen (1998) and Cromdal (2004) report the use of C-S for
resolving disputes among bilingual (Turkish-Danish) children in Denmark and
multilingual (Swedish-English and/or a Non-Scandinavian Language) children in
Sweden respectively.

C-S does not only take place between standard languages but between minority
languages and dialects as well. For example, Themistocleous (2013) studied C-S
between Greek and Cypriot Greek and Deuchar (2006) focused on the C-S between
Welsh and English in the UK. Berruto (2005) reports cases of language mixing
between standard Italian and Italoromance dialects in Italy. In the Balkans,
Kyuchukov (2006) analyzed C-S between Turkish-Bulgarian and Romani in
Bulgaria. C-S between dialects and/or standard vs. minority languages in
computer mediated interaction was analyzed by Siebenhaar (2006) among Swiss-
German dialects and by Robert-Tissot and Morel (2017) through SMS corpora
collected across Germanic (i.e. English and German) and Romance languages
(French, Spanish, Italian) in Switzerland.

C-S is commonly observable across immigrant contexts in Europe. In the UK,
Georgakopoulou and Finnis (2009) described the C-S patterns between English
and Cypriot Greek while Issa (2006) focused on the C-S between English and
Cypriot Turkish communities in London. Wei and Milroy (1995) analyzed the C-S
between English and Chinese from a conversational analysis point of view based
on the interactions of bilingual (Chinese-English) families in Northeastern
England. In addition, Ożańska-Ponikwia (2016) investigated the Polish-English
C-S in the UK as well. C-S among immigrant community members have also been
widely studied in Germany (e.g. Turkish-German C-S by Keim (2008) and
Çetinoğlu (2017), Russian-German C-S by Khakimov (2016)). In the Netherlands,
C-S studies include Turkish-Dutch C-S by Backus (2010) and Dutch-Morroccan C-S
by Nortier (1990). In Belgium, Meeuws and Blommaert (1998) studied the French-
Lingala-Swahili C-S among immigrants of Zaire and Treffers-Daller (1994)
studied French-Dutch C-S in Brussels. In Spain, Jieanu (2013) describes the
Romanian-Spanish C-S among the Romanian immigrants. In addition to the C-S
analyses within spoken interactions of immigrant communities across Europe,
there are also studies about C-S within computer mediated communication as
well. These studies include Greek-German C-S by Androutsopoulos (2015) in
Germany, Turkish-Dutch C-S by Papalexakis et al. (2014), Papalexakis and
Doğruöz (2015) and a comparison of Turkish-Dutch and Moroccan-Dutch C-S by
Dorleijn and Nortier (2009) in the Netherlands. Similarly, Marley (2011)
compared French-Arabic C-S within computer mediated interaction across
Moroccan communities in France and the UK.

In addition to daily communication, some linguists are also interested in the
C-S observed in historical documents. While Swain (2002) explored Latin-Greek
C-S by Cicero (Roman Statesman), Dunkel (2000) analyzed C-S in his
communication with Atticus (Roman philosopher who studied in Athens) in the
Roman Empire. Argenter (2001) reports cases of language mixing within the
Catalan Jewish community (in Spain) in the 14th and 15th centuries and Rothman
(2011) highlights the C-S between Italian, Slavic and Turkish in the
historical documents about Ottoman-Venetian relations. In Switzerland, Volk
and Clematide (2014) worked on detecting and annotating C-S patterns in
diachronic and multilingual (English, French, German, Italian, Romansh and
Swiss German) Alpine Heritage corpus.

Within the media context, Martin (1998) investigated English C-S in written
French advertising, and Onysko (2007) investigated the English C-S in German
written media through corpus analyses. Zhiganova (2016) indicates that German
speakers perceive C-S into English for advertising purposes with both positive
and negative consequences.

Similar to humans, institutions and/or organizations could also have
multilingual communication with their members and/or audience. For example,
Wodak et al. (2012) analyzed the C-S and language choice at the institutional
level for European Union institutions.

##  6 C-S across Languages: Indian Context

According to the 2011 Census Chandramouli (2011), 26% of the population of
India is bilingual, while 7% is trilingual. There are 121 major languages and
1599 other languages in India, out of which 22 (Assamese, Bangla, Bodo, Dogri,
Gujarati, Hindi, Kashmiri, Kannada, Konkani, Maithili, Malayalam, Manipuri,
Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, Sanskrit, Santali, Sindhi,
Urdu) are scheduled languages with an official recognition (almost 97% of the
population speaks one of the scheduled languages). Most of the population (
93%) speak languages from the Indo-Aryan (Hindi, Bengali, Marathi, Urdu,
Gujarati, Punjabi, Kashmiri, Rajasthani, Sindhi, Assamese, Maithili, Odia) and
Dravidian (Kannada, Malayalam, Telugu, Tamil) language families. The census
excludes languages with a population lower than 10,000 speakers. Given this,
it is probably difficult to find monolingual speakers in India considering the
linguistic diversity and wide-spread multilingualism.

Kachru (1978) provides one of the early studies on the types and functions of
C-S in India with a historical understanding of the multilingual context. In
addition to the mutual influences and convergence of Indo-Aryan and Dravidian
languages internally, he mentions Persian and English as outside influences on
Indian languages. Similarly, Sridhar (1978) provides an excellent comparative
overview about the functions of C-S in Kannada in relation to the Perso-Arabic
vs. English influences. Kumar (1986) gives examples about the formal (e.g.
within NPs, PPs, VPs) and functional (i.e. social and stylistic) aspects of
Hindi-English C-S from a theoretical point of view. More recently, Doley
(2013) explains how fish mongers in a local fish market in Assam adjust and
switch between Assamese, English and local languages strategically to sell
their products to multilingual clientele. Another observation about C-S in
daily life comes from Boro (2020) who provides examples of English, Assamese
and Bodo (another language spoken in the Assam region) C-S and borrowings. In
addition to English, Portuguese was also in contact with the local languages
as a result colonization in South India. For example, Kapp (1997) explains the
Portuguese influence through borrowings in Dravidian languages (i.e. Kannada
and Telugu) spoken in India.

Instead of automatic data collection and methods of analyses, the C-S examples
for the above-mentioned studies are (probably) encountered and collected by
the authors themselves in daily life interactions over a period of time with
limited means. Nowadays, these small sets of data would be regarded as
insignificant in computational areas of research. However, ignoring these
studies and data could have serious consequences since crucial information
about the social and cultural dynamics in a multilingual setting would also be
lost. For example, Nadkarni (1975) proves this point by explaining how social
factors influence the C-S between Saraswat Brahmin dialect of Konkani (Indo-
Aryan language) and Kannada (Dravidian language) in the South of India. Both
languages have been in contact with each other for over four hundred years.
Saraswat Brahmins are fluent in both Konkani and Kannada but they do not speak
Konkani with Kannada speakers and they also do not C-S between Konkani and
Kannada. Nadkarni (1975) attributes this preference to the high prestige
associated with Konkani within the given social context. Since Kannada
(perceived as less prestigious) is widely spoken in that region, Konkani
speakers learn and speak Kannada for functional purposes in daily life which
does not involve C-S. However, it is not common for Kannada speakers to learn
and speak Konkani Nadkarni (1975).

C-S in India has been investigated through written media, advertising and film
industry as well. Si (2011) analyzed Hindi-English C-S in the scripts of seven
Bollywood movies which were filmed between 1982 and 2004. Her results indicate
a change of direction C-S over the years. More specifically, Hindi was the
dominant language with occasional switches to English for the early
productions but English became the dominant language especially for younger
generations in the later productions. A similar trend has been observed for
Bengali movie scripts as well. Through analyzing movie scripts (between 1970s
and 2010s), Chatterjee (2016) finds a drastic increase in the use of bilingual
verbs (e.g. _renovate koreche_ “renovation do”) over time and attributes this
rise to the increasing popularity of English in Indian society. Within the
immigrant context, Gardner-Chloros and Charles (2007) focused on the types and
functions of C-S between Hindi and English across the TV programs (e.g. highly
scripted vs. loosely scripted programs) of a British/Asian cable channel in
the UK. Although they have come across C-S in a variety of TV shows, the least
amount of C-S was encountered in the news broadcasts (i.e. highly scripted).
In general, they have encountered less C-S on TV broadcasts in comparison to
the natural speech and attribute this factor to the consciousness of TV
personalities about pure language use (instead of C-S). Similarly, Zipp (2017)
analyzed Gujarati-English C-S within a radio show targeting British South
Asians living in the US and concluded that C-S was part of identity
construction among youngsters (group identity). Pratapa and Choudhury (2017)
perform a quantitative study of 18 recent Bollywood (Hindi) movies and find
that C-S is used for establishing identity, social dynamics between characters
and the socio-cultural context of the movie.

From an advertising point of view, Kathpalia and Wee Ong (2015) analyzed C-S
in Hinglish (i.e. Hindi, English, Urdu, Sanskrit according to their
definition) billboards about the Amul brand in India. After compiling the
advertisements on billboards (1191), they classified the structures and
functions of C-S. Their results indicate more intrasentential C-S than
intersentential ones on the billboards. In terms of function, the advertisers
used C-S to indicate figures of speech (e.g. puns, associations, contradictory
associations, word-creation and repetitions) to attract the attention of the
target group.

Mohanty (2006) provides an extended overview of the multilingual education
system in India exploring the types and quality of schools across a wide
spectrum. In general, high-cost English Medium (EM) education is valued by
upper-class and affluent families. Although low-cost EM education is also
available for lower income families, he questions its impact in comparison to
education in the local languages. Sridhar (2002) explains that C-S is commonly
practiced among students in schools across India. In addition, she finds it
unrealistic to ask the students to separate the two languages harshly. In
immigrant contexts, Martin et al. (2006) investigates how Gujarati-English C-S
is used among the South Asian students in educational settings in the UK.
Another analysis reveals a shift from Bengali toward English among the younger
generations of the immigrant Bengali community in the UK Al-Azami (2006). In
terms of the C-S patterns, first generation immigrants integrate English words
while speaking Bengali whereas English dominates the conversations of younger
generations with occasional switches to Bengali. There are also studies about
Bengali-English C-S in the UK school settings Pagett (2006) and Bangladesh
Obaidullah (2016) as well. However, a systematic comparison between Bengali-
English C-S in India, Bangladesh and immigrant settings are lacking.

In their study about aphasic patients, Shyamala Chengappa and Bhat (2004)
report increased frequency of C-S between Malayalam and English for aphasic
patients in comparison to the control group. However, there were less
differences between the groups in terms of functions of C-S. Deepa and
Shyamala (2019) find that amount and types of C-S could be used to
differentiate between healthy and mild dementia patients who are bilingual in
Kannada and English. Although both studies are carried out with limited
subjects, they offer insights about the use of C-S in health settings as well.

##  7 Computational Approaches to C-S

There has been significant interest in building language technologies for
code-switched languages over the last few years, spanning a diverse range of
tasks such as Language Identification, Part of Speech Tagging, Sentiment
Analysis and Automatic Speech Recognition. In the European language context,
work has mainly focused on Turkish-Dutch, Frisian-Dutch, Turkish-German and
Ukranian-Russian with some initial attempts being made in parsing Russian-Komi
text. In the Indian language context, Hindi-English is the most widely studied
language pair for computational processing, with some recent work on Telugu-
English, Tamil-English, Bengali-English and Gujarati-English. Sitaram et al.
(2019) provide a comprehensive survey of research in computational processing
of C-S text and speech and Jose et al. (2020) present a list of datasets
available for C-S research. However, despite significant efforts, language
technologies are not yet capable of processing C-S as seamlessly as
monolingual data. We identify three main limitations of the current state of
computational processing of C-S: data, evaluation and user-facing
applications.

###  7.1 Data

The use of Deep Neural Networks, which require large amounts of labeled and
unlabeled training data have become the de facto standard for building speech
and NLP systems. Since C-S languages tend to be low resourced, building Deep
Learning-based models is challenging due to the lack of large C-S datasets.
Massive multilingual Language Models (LMs) such as multilingual BERT Devlin et
al. (2019) and XLM-R Conneau et al. (2020) have shown promise in enabling the
coverage of low-resource languages without any labeled data by using the zero-
shot framework. These LMs are typically trained in two phases: a “pre-
training” phase, in which unlabeled data from one or multiple languages may be
used and a “fine-tuning” phase, in which task-specific labeled data is used to
build a system capable of solving the task.

Since multilingual LMs are trained on multiple languages at the same time, it
has been suggested that these models may be capable of processing C-S text
Johnson et al. (2017), with promising results initially reported on POS
tagging Pires et al. (2019). Khanuja et al. (2020) found that multilingual
BERT outperforms older task-specific models on C-S tasks, however, the
performance on C-S is much worse than the performance on the same tasks in a
monolingual setting. Further, these LMs are either trained primarily on
monolingual datasets such as Wikipedia in the case of mBERT, or Common Crawl
111http://www.commoncrawl.org in the case of XLM-R. So, they are either not
exposed to C-S data at all during training, or they miss out on several
language pairs, types and functions of C-S that are encountered in daily life
but not available on the web.

Since massive multilingual LMs are now replacing traditional models across
many NLP applications, it is crucial to consider how they can be trained on
C-S data, or made to work for C-S by incorporating other sources of knowledge.

###  7.2 Evaluation

Much of speech and NLP research is now driven by standard benchmarks that
evaluate models across multiple tasks and languages. Due to the shortage of
standardized datasets for C-S, until recently, the evaluation of C-S models
was performed over individual tasks and language pairs. Khanuja et al. (2020)
and Aguilar et al. (2020) proposed the first evaluation benchmarks for C-S
that span multiple tasks in multiple language pairs. The GLUECoS benchmark
Khanuja et al. (2020) consists of the following C-S tasks in Spanish-English
and Hindi-English: Language Identification (LID), Part of Speech (POS)
tagging, Named Entity Recognition (NER), Sentiment Analysis, Question
Answering and Natural Language Inference (NLI). The LINCE benchmark Aguilar et
al. (2020) covers Language Identification, Named Entity Recognition, Part-of-
Speech Tagging, and Sentiment Analysis in four language pairs: Spanish-
English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian
Arabic.

Although these benchmarks are important starting points for C-S, it is clear
that they do not represent the entire spectrum of C-S, both from the point of
view of potential applications and language pairs. Further, it is important to
note that while state-of-the-art models perform well on tasks such as LID, POS
tagging and NER, they are only slightly better than chance when it comes to
harder tasks like NLI, showing that current models are not capable of
processing C-S language. Moreover, many of the C-S tasks in the benchmarks
above consist of annotated tweets, which only represent a certain type of C-S.
Due to these limitations, we currently do not have an accurate picture of how
well models are able to handle C-S.

###  7.3 User-facing applications

Although speech and NLP models for C-S have been built for various
applications, a major limitation of the work done so far in computational
processing of C-S is the lack of end-to-end user-facing applications that
interact directly with users in multilingual communities. For example, there
is no widely-used spoken dialogue system that can understand as well as
produce code-switched speech, although some voice assistants may recognize and
produce C-S in limited scenarios in some locales. Although computational
implementations of grammatical models of C-S exist Bhat et al. (2016), they do
not necessarily generate natural C-S utterances that a bilingual speaker would
produce Pratapa et al. (2018). Most crucially, current computational
approaches to C-S language technologies do not usually take into account the
linguistic and social factors that influence why and when speakers/users
choose to code-switch.

Bawa et al. (2020) conducted a Wizard-of-Oz study using a Hindi-English
chatbot and found that not only did bilingual users prefer chatbots that could
code-switch, they also showed a preference towards bots that mimicked their
own C-S patterns. Rudra et al. (2016) report a study on 430k tweets from
Hindi-English bilingual users and find that Hindi is preferred for the
expression of negative sentiment. In a follow-up study, Agarwal et al. (2017)
find that Hindi is the preferred language for swearing in Hindi-English C-S
tweets, and swearing may be a motivating factor for users to switch to Hindi.
The study also finds a gender difference, with women preferring to swear in
English more often than Hindi. Such studies indicate that multilingual
chatbots and intelligent agents need to be able to adapt to users’ linguistic
styles, while also being capable of determining when and how to code-switch.

Due to the paucity of user-facing systems and standard benchmarks covering
only a handful of simpler NLP tasks, it is likely that we overestimate how
well computational models are able to handle C-S. In sum, language
technologies for C-S seem to be constrained by the lack of availability of
diverse C-S training data, evaluation benchmarks and the absence of user-
facing applications. They need to go beyond pattern recognition and
grammatical constraints of C-S in order to process and produce C-S the way
humans do. Hence, it is important for the CL community to be aware of the vast
literature around C-S in linguistics, particularly as we proceed to solving
more challenging tasks.

##  8 Conclusion

The goal of this paper was to inform computational linguists and language
technologists about the linguistic and social aspects C-S studies focusing on
the European and Indian multilingual contexts. There are some similarities
(e.g. themes for linguistic research in C-S) but also differences between the
two contexts in terms of the social, cultural and historical characteristics.
For example, C-S in immigrant communities has been a common theme for both
multilingual contexts. In Europe, C-S has been widely studied within the
immigrant communities who have come through labor immigration in the 1960s.
However, there is a need for more research about the C-S in immigrant
languages with a more recent history as well as minority languages and
regional dialects. Analyzing C-S in the immigration context is even more
complex for Indian languages. There are hardly any systematic linguistic
comparisons between the C-S within the same language pairs in India and
immigrant contexts (e.g. C-S between Hindi-English in India vs. Hindi-English
in the US/UK). There is also a need for more research about C-S between less
known language pairs in India. However, some of these languages are not even
officially listed (e.g. in census results) since they have less than 10,000
speakers. In these cases, collecting and analyzing the multilingual and C-S
data becomes more difficult.

A common flaw that is shared both by linguistics and computational areas of
research is to focus only on the positive evidence and assume that C-S will
occur in all multilingual contexts. However, there is also a need for negative
evidence to falsify this assumption. As illustrated through an example from
Konkani-Kannada language contact in India (see section 6), bilingual speakers
may prefer not to C-S due to historical, social and cultural factors operating
in that setting. Therefore, developing an automatic C-S system for a random
pair of languages without an in-depth and systematic analysis of linguistic
and social aspects of C-S in a particular context would not be very useful for
the targeted users and/or language technologists.

To date, the literature focusing on the social and linguistic aspects of C-S
is less visible for CL researchers. This lack of visibility leads to
misunderstandings and/or incomplete citations of earlier research which would
have saved time and resources for CL research if resolved. One of the reasons
is perhaps the differences in publishing traditions between humanities and
computational areas of research. Conference and workshop proceedings are
commonly accepted means of publication in computational linguistics. Whereas,
journal publications, books and/or chapters are the publication forms in
humanities. However, guidelines about how to cite publications in humanities
are often missing in computational venues. There are also differences in terms
of length, review cycles and open access policies between the two fields which
may influence the visibility of research output for each other. It is perhaps
useful to remember that science advances by standing on the shoulders of
giants (i.e. building upon earlier research). With our contribution to the
conference, we hope to connect the two fields and start a scientific dialogue
to enhance the advancement in both fields.

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