# The Decades Progress on Code-Switching Research in NLP:
A Systematic Survey on Trends and Challenges

Genta Indra Winata1, Alham Fikri Aji2, Zheng-Xin Yong3, Thamar Solorio1
1Bloomberg 2MBZUAI 3Brown University
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>The
work was done while at Bloomberg.

###### Abstract

Code-Switching, a common phenomenon in written text and conversation, has been
studied over decades by the natural language processing (NLP) research
community. Initially, code-switching is intensively explored by leveraging
linguistic theories and, currently, more machine-learning oriented approaches
to develop models. We introduce a comprehensive systematic survey on code-
switching research in natural language processing to understand the progress
of the past decades and conceptualize the challenges and tasks on the code-
switching topic. Finally, we summarize the trends and findings and conclude
with a discussion for future direction and open questions for further
investigation.

##  1 Introduction

Code-Switching is the linguistic phenomenon where multilingual speakers use
more than one language in the same conversation Poplack (1978). The fragment
of the worldwide population that can be considered multilingual, i.e., speaks
more than one language, far outnumbers monolingual speakers Tucker (2001);
Winata et al. (2021a). This alone makes a compelling argument for developing
NLP technology that can successfully process code-switched (CSW) data.
However, it was not until the last couple of years that CSW-related research
became more popular Sitaram et al. (2019); Jose et al. (2020); Doğruöz et al.
(2021), and this increased interest has been motivated to a large extent by:
1) The need to process social media data. Before the proliferation of social
media platforms, it was more common to observe code-switching in spoken
language and not so much in written language. This is not the case anymore, as
multilingual users tend to combine the languages they speak on social media;
2) The increasing release of voice-operated devices. Now that smart assistants
are becoming more and more accessible, we have started to realize that
assuming users will interact with NLP technology as monolingual speakers is
very restrictive and does not fulfill the needs of real-world users.
Multilingual speakers also prefer to interact with machines in a CSW manner
Bawa et al. (2020). We show quantitative evidence of the upward trend for CSW-
related research in Figure 1.

Figure 1: Number of publications over time in *CL and ISCA venues. We collect
the papers on October 2022. Top: Relative to all *CL and ISCA papers. Bottom:
absolute number, broken down into conferences vs workshops. It does not
include papers published after. The graphs do not show the number of
publications published in journals and symposiums.

Category | Options
---|---
Languages | Bilingual, Trilingual, 4+
Venues | Conference, Workshop, Symposium, Book
Papers | Theory / Linguistics, Empirical, Analysis, Position/Opinion/Survey, Metric, Corpus, Shared Task, Demo
Datasets | Social Media, Speech (Recording), Transcription, News, Dialogue, Books, Government Document, Treebank
Methods | Rule/Linguistic Constraint, Statistical Model, Neural Network, Pre-trained Model
Tasks |  Text:  Topic Modeling, Semantic Parsing, Dependency Parsing, Sentiment Analysis, Emotion Detection,
| Abusive Language Detection, Sarcasm Detection, Humor Detection, Humor
Generation, Dialogue State Tracking,
| Text Generation, Natural Language Understanding, Named Entity Recognition,
Part-of-Speech Tagging,
| Natural Language Entailment, Language Modeling, Regression, Language
Identification, Machine Translation,
| Text Normalization, Micro-Dialect Identification, Question Answering,
Summarization
|  Speech:  Acoustic Modeling, Speech Recognition, Text-to-Speech, Speech
Synthesis

Table 1: Categories in the annotation scheme.

In this paper, we present the first large-scale comprehensive survey on CSW
NLP research in a structured manner by collecting more than 400 papers
published on open repositories, such as the ACL Anthology and ISCA proceedings
(see §2). We manually coded these papers to collect coarse- and fine-grained
information (see §2.1) on CSW research in NLP that includes languages covered
(see §3), NLP tasks that have been explored, and new and emerging trends (see
§4). In addition, motivated by the fact that fields like linguistics, socio-
linguistics, and related fields, have studied CSW since the early 1900s, we
also investigate to what extent theoretical frameworks from these fields have
influenced NLP approaches (see §5), and how the choice of methods has evolved
over time (see §5.4). Finally, we discuss the most pressing research
challenges and identify a path forward to continue advancing this exciting
line of work (see §6).

The area of NLP for CSW data is thriving, covering an increasing number of
language combinations and tasks, and it is clearly advancing from a niche
field to a common research topic, thus making our comprehensive survey timely.
We expect the survey to provide valuable information to researchers new to the
field and motivate more research from researchers already engaging in NLP for
CSW data.

Figure 2: Language Categories. *NE denotes Non English. We show fine-grained
categories in green and blue.

##  2 Exploring Open Proceedings

To develop a holistic understanding of the trends and advances in CSW NLP
research, we collect research papers on CSW from the ACL Anthology and ISCA
proceedings. We focus on these two sources because they encompass the top
venues for publishing in speech and language processing in our field. In
addition, we also look into personal repositories from researchers in the
community that contains a curated list of CSW-related papers. We discuss below
the search process for each venue.

#### ACL Anthology

We crawled the entire the ACL Anthology repository up to October
2022.111https://github.com/acl-org/acl-anthology We then filtered papers by
using the following keywords related to CSW: “codeswitch", “code switch",
“code-switching", “code-switched", “code-switch", “code-mix", “code-mixed",
“code-mixing", “code mix" “mixed-language", “mixed-lingua", “mixed language",
“mixed lingua", and “mix language".

#### ISCA Proceedings

We manually reviewed publicly available proceedings on the ISCA
website222https://www.isca-speech.org and searched for papers related to CSW
using the same set of keywords as above.

Figure 3: (Top): Number of publications across the type of language
combination (bilingual, trilingual or 4+. (Bottom): Number of publications on
fine-grained bilingual category with English as the L2 language. Figure 4:
Number of publications of bilingual code-switched languages that do not
contain English. ∗msa stands for Modern Standard Arabic. The first two are the
combination of a language with its dialect.

#### Web Resources

To extend the coverage of paper sources, we also gathered data from existing
repositories.333https://github.com/gentaiscool/code-switching-
papers444https://genius1237.github.io/emnlp19_tut/ We can find multiple
linguistics papers studying about CSW.

###  2.1 Annotation Process

We have three human annotators to annotate all collected papers based on
multiple categories shown in Table 1. All papers are coded by a least one
annotator. To extract the specific information we are looking for from the
paper, the annotator needs to read through the paper, as most of the
information is not contained in the abstract. The full list of the annotations
we collected is available in the Appendix (see §A).

To facilitate our analysis, we annotated the following aspects:

  * •

Languages: CSW is not restricted to pairs of languages; thus, we divide papers
by the number of languages that are covered into bilingual, trilingual, and 4+
(if there are at least four languages). For a more fine-grained classification
of languages, we categorize them by geographical location (see Figure 2).

  * •

Venues: There are multiple venues for CSW-related publications. We considered
the following type of venues: conference, workshop, symposium and book. As we
will discuss later, the publication venue is a reasonable data point of the
wider spread of CSW research in recent years.

  * •

Papers: We classify the paper types based on their contribution and nature. We
predict that we will have a high distribution of dataset/resource papers, as
lack of resources has been a major bottleneck in the past.

  * •

Datasets: If the paper uses a dataset for the research, we will identify the
source and modality (i.e., written text or speech) of the dataset.

  * •

Methods: We identify the type of methods presented in work.

  * •

Tasks: We identify the downstream NLP tasks (including the speech processing-
related tasks) presented in work.

Languages | # Publications
---|---
*CL | ISCA | Total | Shared Task
Hindi-English | 111 | 17 | 128 | 30
Spanish-English | 78 | 8 | 86 | 40
Chinese-English‡ | 20 | 27 | 47 | 5
Tamil-English | 37 | 2 | 39 | 17
Malayalam-English | 23 | 2 | 25 | 13

Table 2: Most common code-switching languages in *CL and ISCA venues. ‡The
count does not include the dialect or South East Asian Mandarin - English Lyu
et al. (2010a) since they contain more than two languages (i.e., it has words
from the Chinese dialect).

##  3 Language Diversity

Here, we show the languages covered in the CSW resources. While focusing on
the CSW phenomenon increases diversity of NLP technology, as we will see in
this section, future efforts are needed to provide significant coverage of the
most common CSW language combinations worldwide.

Languages | # Publications
---|---
| non-ST | ST | Total
Language Identification | 46 | 17 | 63
Sentiment Analysis | 31 | 30 | 61
NER | 17 | 14 | 31
POS Tagging | 29 | 1 | 30
Abusive/Offensive Lang. Detection | 9 | 16 | 25
ASR | 20 | 0 | 22
Language Modeling | 19 | 1 | 20
Machine Translation | 8 | 5 | 13

Table 3: Most common tasks in ACL venues. ST denotes shared task.

###  3.1 Variety of Mixed Languages

Figure 3 shows the distribution of languages represented in the NLP for CSW
literature. Most of the papers use datasets with two language pairs. However,
we did find a few papers that address CSW scenarios with more than two
languages. We consider this a relevant future direction in CSW: scaling model
abilities to cover n languages, with $n\geq 2$.

#### CSW in two languages

We group the number of publications focusing on bilingual CSW based on world
regions in Figure 3 (bottom). We can see that the majority of research in CSW
has focused on South Asian-English, especially on Hindi-English, Tamil-
English, and Malayalam-English, as shown in Table 2. The other common language
pairs are Spanish-English and Chinese-English. That table also shows that many
of the publications are shared task papers. This is probably reflecting
efforts from a few research groups to motivate more research into CSW, such as
that behind the CALCS workshop series.

Looking at the languages covered, we also find that there are many language
pairs that come from different language families, such as Turkish-German
Çetinoğlu (2016); Çetinoğlu and Çöltekin (2019); Özateş and Çetinoğlu (2021);
Özateş et al. (2022), Turkish-Dutch Gambäck and Das (2016), French-Arabic
Sankoff (1998); Lounnas et al. (2021), Russian-Tatar Taguchi et al. (2021),
Russian-Kazakh Mussakhojayeva et al. (2022a), Hindi-Tamil Thomas et al.
(2018b), Arabic-North African El-Haj et al. (2018), Basque-Spanish Aguirre et
al. (2022), and Wixarika-Spanish Mager et al. (2019). There are only very few
papers working on Middle Eastern - English language pairs, most of the time,
the Middle Eastern languages are mixed with non-English and/or dialects of
these languages (see Figure 4).

#### Trilingual

The number of papers addressing CSW in more than two languages is still small
(see 3 top), compared to the papers looking at pairs of languages. Not
surprisingly, this smaller number of papers focus on world regions where
either the official languages are more than two, or these languages are widely
used in the region, for example, Arabic-English-French Abdul-Mageed et al.
(2020), Hindi-Bengali-English Barman et al. (2016), Tulu-Kannada-English Hegde
et al. (2022), and Darija-English-French Voss et al. (2014).

#### 4+

When looking at the papers that focus on more than three languages, we found
that many papers use South East Asian Mandarin-English (SEAME) dataset Lyu et
al. (2010a), which has Chinese dialects and Malay or Indonesian words. Most of
the other datasets are machine-generated using rule-based or neural methods.

###  3.2 Language-Dialect Code-Switching

Based on Figure 4, we can find some papers with language-dialect CSW, such as
Chinese-Taiwanese Dialect Chu et al. (2007); Yu et al. (2012) and Modern
Standard Arabic (MSA)-Arabic Dialect Elfardy and Diab (2012); Samih and Maier
(2016); El-Haj et al. (2018). The dialect, in this case, is the variation of
the language with a different form that is very specific to the region where
the CSW style is spoken.

##  4 Tasks and Datasets

In this section, we summarize our findings, focusing on the CSW tasks and
datasets. Table 3 shows the distribution of CSW tasks for ACL papers with at
least ten publications. The two most popular tasks are language identification
and sentiment analysis. Researchers mostly use the shared tasks from 2014
Solorio et al. (2014) and 2016 Molina et al. (2016) for language
identification, and the SemEval 2020 shared task Patwa et al. (2020) for
sentiment analysis. For ISCA, the most popular tasks are unsurprisingly ASR
and TTS. This strong correlation between task and venue shows that the speech
processing and *CL communities remain somehow fragmented and working in
isolation from one another, from the most part.

| # Publications
---|---
| *CL | ISCA | Total
Public Dataset | 38 | 4 | 42
Private Dataset | 54 | 18 | 72

Table 4: Publications that introduce new corpus.

Source | *CL | ISCA | Total
---|---|---|---
Social Media | 183 | 3 | 186
Speech (Recording) | 29 | 102 | 141
Transcription | 23 | 4 | 27
News | 19 | 5 | 24
Dialogue | 16 | 2 | 18
Books | 7 | 1 | 8
Government Document | 6 | 0 | 6
Treebank | 5 | 0 | 5

Table 5: The source of the CSW dataset in the literature.

#### Public vs. Private Datasets

Public datasets availability also dictates what tasks are being explored in
CSW research. Public datasets such as HinGE Srivastava and Singh (2021b),
SEAME Lyu et al. (2010a) and shared task datasets Solorio et al. (2014);
Molina et al. (2016); Aguilar et al. (2018); Patwa et al. (2020) have been
widely used in many of the papers. Some work, however, used new datasets that
are not publicly available, thus hindering adoption (see Table 4). There are
two well-known benchmarks in CSW: LinCE Aguilar et al. (2020) and GlueCOS
Khanuja et al. (2020b). These two benchmarks have a handful of tasks, and they
are built to encourage transparency and reliability of evaluation since the
test set labels are not publicly released. The evaluation is done
automatically on their websites. However, their support languages are mostly
limited to popular CSW language pairs, such as Spanish-English, Modern
Standard Arabic-Egyptian, and Hindi-English, the exception being Nepali-
English in LinCE.

#### Dataset Source

Table 5 shows the statistics of dataset sources in the CSW literature. We
found that most of the ACL papers were working on social media data. This is
expected, considering that social media platforms are known to host informal
interactions among users, making them reasonable places for users to code-
switch. Naturally, most ISCA papers work on speech data, many of which are
recordings of conversations and interviews. There are some datasets that come
from speech transcription, news, dialogues, books, government documents, and
treebanks.

#### Paper Category

Table 6 presents the distribution of CSW papers. Most of the papers are
empirical work focusing on the evaluation of downstream tasks. The second
largest population is shared tasks. We also notice that many papers introduce
new CSW corpus, but they are not released publicly. Some papers only release
the URL or id to download the datasets, especially for datasets that come from
social media (e.g., Twitter) since redistribution of the actual tweets is not
allowed Solorio et al. (2014); Molina et al. (2016) resulting in making
reproducibility harder. Social media users can delete their posts at any point
in time, resulting in considerable data attrition rates. There are very few
papers working on the demos, theoretical work, position papers, and
introducing evaluation metrics.

##  5 From Linguistics to NLP

Notably, papers are working on approaches that are inspired by linguistic
theories to enhance the processing of CSW text. In this survey, we find three
linguistic constraints that are used in the literature: equivalence
constraint, matrix-embedded language Framework (MLF), and Functional Head
Constraint. In this section, we will briefly introduce the constraints and
list the papers that utilize the constraints.

Type | *CL | ISCA | Total
---|---|---|---
Empirical | 205 | 100 | 305
Shared Task | 82 | 1 | 83
Corpus (Closed) | 54 | 18 | 62
Corpus (Open) | 38 | 4 | 42
Analysis | 34 | 8 | 42
Demo | 7 | 2 | 9
Theoretical/Linguistic | 7 | 0 | 7
Position/Opinion/Survey | 3 | 0 | 3
Metric | 2 | 1 | 3

Table 6: Paper Type of the CSW papers.

###  5.1 Linguistic-Driven Approaches

#### Equivalence Constraint

In a well-formed code-switched sentence, the switching takes place at those
points where the grammatical constraints of both languages are satisfied
Poplack (1980). Li and Fung (2012, 2013) incorporate this syntactic constraint
to a statistical code-switch language model (LM) and evaluate the model on
Chinese-English code-switched speech recognition. On the same line of work,
Pratapa et al. (2018a); Pratapa and Choudhury (2021) implement the same
constraint to Hindi-English CSW data by producing parse trees of parallel
sentences and matching the surface order of child nodes in the trees. Winata
et al. (2019c) apply the constraint to generate synthetic CSW text and find
that combining the real CSW data with synthetic CSW data can effectively
improve the perplexity. They also treat parallel sentences as a linear
structure and only allow switching on non-crossing alignments.

#### Matrix-Embedded Language Framework (MLF)

Myers-Scotton (1997) proposed that in bilingual CSW, there exists an
asymmetrical relationship between the dominant matrix language and the
subordinate embedded language. Matrix language provides the frame of the
sentence by governing all or most of the most of the grammatical morphemes as
well as word order, whereas syntactic elements that bear no or only limited
grammatical function can be provided by the embedded language Johanson (1999);
Myers-Scotton (2005). Lee et al. (2019a) use augmented parallel data by
utilizing MLF to supplement the real code-switch data. Gupta et al. (2020) use
MLF to automatically generate the code-mixed text from English to multiple
languages without any parallel data.

#### Functional Head Constraint

Belazi et al. (1994) posit that it is impossible to switch languages between a
functional head and its complement because of the strong relationship between
the two constituents. Li and Fung (2014) use the constraint of the LM by first
expanding the search network with a translation model and then using parsing
to restrict paths to those permissible under the constraint.

###  5.2 Learning from Data Distribution

Linguistic constraint theories have been used for decades to generate
synthetic CSW sentences to address the lack of data issue. However, the
approach requires external word alignments or constituency parsers that create
erroneous results instead of applying the linguistic constraints to generate
new synthetic CSW data, building a pointer-generator model to learn the real
distribution of code-switched data Winata et al. (2019c). Chang et al. (2019)
propose to generate CSW sentences from monolingual sentences using Generative
Adversarial Network (GAN) Goodfellow et al. (2020) and the generator learns to
predict CSW points without any linguistic knowledge.

###  5.3 The Era of Statistical Methods

The research on CSW is also influenced by the progress and development of
machine learning. According to Figure 5, starting in 2006, statistical methods
have been adapted to CSW research, while before that year, the approaches were
mainly rule-based. There are common statistical methods for text
classification used in the literature, such as Naive Bayes Solorio and Liu
(2008a) and Support Vector Machine (SVM) Solorio and Liu (2008b). Conditional
Random Field (CRF) Sutton et al. (2012) is also widely seen in the literature
for sequence labeling, such as Part-of-Speech (POS) tagging Vyas et al.
(2014), Named Entity Recognition (NER), and word-level language identification
Lin et al. (2014); Chittaranjan et al. (2014); Jain and Bhat (2014). HMM-based
models have been used in speech-related tasks, such as speech recognition
Weiner et al. (2012a); Li and Fung (2013) and text synthesis Qian et al.
(2008); Shuang et al. (2010); He et al. (2012).

###  5.4 Utilizing Neural Networks

(a) *CL

(b) ISCA

Figure 5: Methods used for code-mixing NLP.

Following general NLP trends, we see the adoption of neural methods and pre-
trained models growing in popularity over time. In contrast, the statistical
and rule-based approaches are diminishing. Compared to ISCA, we see more
adaptation of the pre-training model. This is because ACL work is more text-
based focused, where pre-trained LMs are more widely available.

#### Neural-Based Models

Figure 5 shows that the trend of using neural-based models started in 2013,
and the usage of rule/linguistic constraint and statistical methods diminished
gradually through time, but they are still used even with a low percentage.
RNN and LSTM architectures are commonly used in sequence modeling, such as
language modeling Adel et al. (2013); Vu and Schultz (2014); Adel et al.
(2014c); Winata et al. (2018a); Garg et al. (2018a); Winata et al. (2019c) and
CSW identification Samih et al. (2016a). DNN-based and hybrid HMM-DNN models
are used in speech recognition models Yilmaz et al. (2018); Yılmaz et al.
(2018).

#### Pre-trained Embeddings

Pre-trained embeddings are used to complement neural-based approaches by
initializing the embedding layer. Common pre-trained embeddings used in the
literature are monolingual subword-based embeddings, FastText Joulin et al.
(2016), and aligned-embeddings MUSE Conneau et al. (2017). A standard method
to utilize monolingual embeddings is to concatenate or sum two or more
embeddings from different languages Trivedi et al. (2018). A more recent
approach is to apply an attention mechanism to merge embeddings and form meta-
embeddings Winata et al. (2019a, b). Character-based embeddings have also been
explored in the literature to address the out-of-vocabulary issues on word-
embeddings Winata et al. (2018b); Attia et al. (2018); Aguilar et al. (2021).
Another approach is to train bilingual embeddings using real and synthetic CSW
data Pratapa et al. (2018b). In the speech domain, Lovenia et al. (2022)
utilize wav2vec 2.0 Baevski et al. (2020) as a starting model before fine-
tuning.

#### Language Models

Many pre-trained model approaches utilize multilingual LMs, such as mBERT or
XLM-R to deal with CSW data Khanuja et al. (2020b); Aguilar and Solorio
(2020); Pant and Dadu (2020); Patwa et al. (2020); Winata et al. (2021a).
These models are often fine-tuned with the downstream task or with CSW text to
better adapt to the languages. Some downstream fine-tuning approaches use
synthetic CSW data due to a lack of available datasets. Aguilar et al. (2021)
propose a character-based subword module (char2subword) of the mBERT that
learns the subword embedding that is suitable for modeling the noisy CSW text.
Winata et al. (2021a) compare the performance of the multilingual LM versus
the language-specific LM for CSW context. While XLM-R provides the best
result, it is also computationally heavy. There needed to be more exploration
of larger models.

We see that pre-trained LMs provide better empirical results on current
benchmark tasks and enables an end-to-end approach. Therefore, one can
theoretically work on CSW tasks without any linguistic understanding of the
language, assuming the dataset for model finetuning is available. However, the
downside is that there is little understanding of how and when the LMs would
fail, thus we encourage more interpretability work on these LMs in CSW
setting.

##  6 Recent Challenges and Future Direction

###  6.1 More Diverse Exploration on Code-Switching Styles and Languages

A handful of languages, such as Spanish-English, Hindi-English, or Chinese-
English, dominate research and resource CSW. However, there are still many
countries and cultures rich in the use of CSW, which is still under-
represented in NLP research Joshi et al. (2020); Aji et al. (2022); Yong et
al. (2023), especially on different CSW variations. CSW style can vary in
different regions of the world, and it would be interesting to gather more
datasets on unexplored and unknown styles, which can be useful for further
research and investigation on linguistics and NLP. Therefore, one future
direction is to broaden the language scope of CSW research.

###  6.2 Datasets: Access and Sources

According to our findings, there are more than 60% of the datasets are private
(see Table 4), and they are not released to the public. This eventually
hampers the progress of CSW research, particularly in the results’
reproducibility, credibility, and transparency. Moreover, many studies in the
literature do not release the code to reproduce their work. Therefore, we
encourage researchers who build a new corpus to release the datasets publicly.
In addition, the fact that some researchers provide urls to download the data
is also problematic due to the data attrition issue we raised earlier. Data
attrition is bad for reproducibility, but it is also a waste of annotation
efforts. Perhaps we should work on identifying alternative means to collect
written CSW data in an ecologically valid manner.

###  6.3 Model Scaling

To the best of our knowledge, little work has been done on investigating how
well the scaling law holds for code-mixed datasets. Winata et al. (2021a)
demonstrate that the XLM-R-large model outperforms smaller pre-trained models
on the NER and POS tasks in LinCE benchmark Aguilar et al. (2020); however,
the largest model in the study, which is the XLM-R-large model, only has 355
million parameters. Furthermore, they find that smaller models that combine
word, subword, and character embeddings achieve comparable performance as
mBERT while being faster in inference. Given the recent release of billion-
sized large pre-trained multilingual models such as XGLM and BLOOM Scao et al.
(2022), we urge future research to study the scaling law and performance-
compute trade-off in code-mixing tasks.

###  6.4 Zero-Shot and Few-Shot Exploration

The majority of pre-trained model approaches fine-tune their models to the
downstream task. On the other hand, CSW data is considerably limited. With the
rise of multilingual LMs, especially those that have been fine-tuned with
prompt/instruction Muennighoff et al. (2022); Ouyang et al. (2022); Winata et
al. (2022), one direction is to see whether these LMs can handle CSW input in
a zero-shot fashion. This work might also tie in with model scaling since
larger models have shown better capability at zero-shot and few-shot settings
Winata et al. (2021b); Srivastava et al. (2022).

###  6.5 Robustness Evaluation

Since CSW is a widely common linguistic phenomenon, we argue that cross-
lingual NLP benchmarks, such as XGLUE Liang et al. (2020) and XTREME-R Ruder
et al. (2021), should incorporate linguistic CSW evaluation Aguilar et al.
(2020); Khanuja et al. (2020b). The reasons are that CSW is a cognitive
ability that multilingual human speakers can perform with ease Beatty-Martínez
et al. (2020). CSW evaluation examines the robustness of multilingual LMs in
learning cross-lingual alignment of representations Conneau et al. (2020);
Libovickỳ et al. (2020); Pires et al. (2019); Adilazuarda et al. (2022). On
the other hand, catastrophic forgetting is observed in pre-trained models Shah
et al. (2020), and human speakers (Hirvonen and Lauttamus, 2000; Du Bois,
2009, known as language attrition) in a CSW environment. We argue that
finetuning LMs on code-mixed data is a form of continual learning to produce a
more generalized multilingual LM. Thus, we encourage CSW research to report
the performance of finetuned models on both CSW and monolingual texts.

###  6.6 Task Diversity

We encourage creating reasoning-based tasks for CSW texts for two reasons.
First, code-mixed datasets for tasks such as NLI, coreference resolution, and
question-answering are much fewer in comparison to tasks such as sentiment
analysis, parts-of-speech tagging, and named-entity recognition. Second,
comprehension tasks with the CSW text present more processing costs for human
readers Bosma and Pablos (2020).

###  6.7 Conversational Agents

There has been a recent focus on developing conversational agents with LMs
such as ChatGPT555https://openai.com/blog/chatgpt/, Whisper Radford et al.
(2022), SLAM Bapna et al. (2021), mSLAM Bapna et al. (2022). We recommend
incorporating the capability of synthesizing code-mixed data in human-machine
dialogue, as CS is a prevalent communication style among multilingual speakers
Ahn et al. (2020), and humans prefer chatbots with such capability Bawa et al.
(2020).

###  6.8 Automatic Evaluation for Generation

With the rise of pre-trained models, generative tasks gained more popularity.
However, when generating CSW data, most work used human evaluation for
measuring quality of the generated data. Alternative automatic methods for CS
text based on word-frequency and temporal distribution are commonly used
Guzmán et al. (2017); Mave et al. (2018), but we believe there is still much
room for improvement in this respect. One possible future direction is to
align the evaluation metrics to human judgement of quality Hamed et al. (2022)
where we can assess separately the “faithfulness” of the resulting CSW data
from other desired properties of language generation. Other nuances here are
related to the intricacy of CSW patterns, where ideally the model would mimic
the CSW style of the intended users.

##  7 Conclusion

We present a comprehensive systematic survey on code-switching research in
natural language processing to explore the progress of the past decades and
understand the existing challenges and tasks in the literature. We summarize
the trends and findings and conclude with a discussion for future direction
and open questions for further investigation. We hope this survey can
encourage and lead NLP researchers in a better direction on code-switching
research.

## Limitations

The numbers in this survey are limited to papers published in the ACL
Anthology and ISCA Proceedings. However, we also included papers as related
work from other resources if they are publicly available and accessible. In
addition, the category in the survey does not include the code-switching type
(i.e., intra-sentential, inter-sentential, etc.) since some papers do not
provide such information.

## Ethics Statement

We use publicly available data in our survey with permissive licenses. No
potential ethical issues in this work.

## Acknowledgements

Thanks to Igor Malioutov for the insightful discussion on the paper.

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