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

Paper · 2021 · A. Seza Doğruöz, Sunayana Sitaram, Barbara E. Bullock et al.

Summary

Computational research on code-switching (C-S) has grown fast, but it mostly refines methods and overlooks decades of linguistic work on how and why multilinguals mix languages. This survey, by linguists and an NLP researcher, tries to close that gap. It reviews structural accounts of C-S. These include the Matrix Language Frame (MLF) model that most NLP work relies on, and alternatives such as the Equivalence Constraint, the Functional Head Constraint and constraint-free theories. It separates C-S from borrowing, loan translation, transfer and mixed languages. It also covers the social and pragmatic reasons people switch, drawing on studies from European and Indian multilingual settings. On the technology side, the authors argue that large language models handle diverse C-S types poorly for three reasons: training data is scarce and narrow, benchmarks cover only a few simple tasks, and end-to-end user-facing systems that respect sociolinguistic factors are missing. Their central claim is that the field probably overestimates model competence on C-S. They argue that closer work with linguists, including proper citation of humanities literature, would save effort and produce better tools.

Key points

  • NLP work on C-S mostly assumes the MLF model (Joshi 1982; Myers-Scotton 1997), which is disputed. Bullock et al. (2018) found that different ways of identifying the matrix language agree reliably only on sentences with simple single-word insertions.
  • Li and Fung (2014) and Bhat et al. (2016) reported accuracy gains when the Functional Head Constraint was built into computational and speech models. Some NLP tag sets merge functional items into lexical classes, which makes the annotation too coarse for studying C-S.
  • Hindi-English studies show that language choice carries meaning:
    • Rudra et al. (2016) analysed 430k tweets and found Hindi preferred for negative sentiment.
    • Agarwal et al. (2017) found Hindi preferred for swearing, with women swearing in English more often.
    • In a Wizard-of-Oz study, Bawa et al. (2020) found users preferred chatbots that code-switch, especially ones that copy the user's own switching style.
  • Lone-word insertions are mostly nonce borrowings rather than true switching. Auer (1999) describes a continuum from C-S through Language Mixing to Mixed Languages.
  • The Ukrainian–Russian Surzhyk is described as lying between C-S and a mixed language (Bilaniuk, 2004).

Interesting ideas

  • I1. Treat language choice as a signal, not noise. Switches often mark sentiment, swearing, quoting or group identity. Features for which language is used where could help sentiment or dialogue models in any bilingual setting, including Ukrainian–Russian data.
  • I2. Use linguistically finer annotation. Tag sets should distinguish functional elements such as auxiliaries and negation from lexical words. This lets syntactic constraints on switching be tested and exploited, rather than hidden inside coarse POS classes.
  • I3. Separate insertion from real alternation. A single foreign word and a clause-level switch need different handling. Evaluating them separately would show where systems actually struggle, instead of letting easy insertions inflate scores.
  • I4. Collect negative evidence. Some bilingual communities deliberately avoid switching, as in the paper's Konkani-Kannada example. Checking whether C-S actually occurs in the target setting should come before building a C-S system for an arbitrary language pair.
  • I5. Adapt to each user's switching style. The chatbot finding suggests that dialogue systems should model and mirror how each user mixes languages, not apply one fixed pattern.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓The University of Texas at AustinAustin · United States72 works · 55,722 citations🎓Ghent UniversityGhent · Belgium18 works · 2,888 citations🏢Microsoft ResearchBengaluru · India9 works · 3,523 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 467 works

Cite

Cite as A. Seza Doğruöz, Sunayana Sitaram, Barbara E. Bullock and Almeida Jacqueline Toribio. 2021. A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.acl-long.131

@inproceedings{dogruoz2021survey,
  title = {{A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies}},
  author = {A. Seza Doğruöz, Sunayana Sitaram, Barbara E. Bullock and Almeida Jacqueline Toribio},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.131},
  url = {https://doi.org/10.18653/v1/2021.acl-long.131}
}

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