| Challenge: | Existing measures of code-switching (CS) complexity are word-based, meaning any word is equally likely to switch between any two words. |
| Approach: | They adapt two NLP metrics, multilinguality and CS probability, and put forward Intonation Units (IUs) as basic tokens for transcribed bilingual speech. |
| Outcome: | The proposed measures account for prosodic and prosodic constraints on CS in bilingual speech. |
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| Challenge: | Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. |
| Approach: | They synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI’s Whisper, using a language-to-text translation dataset, CoVoST 2. |
| Outcome: | The proposed model outperforms two monolingual models and is better at code-switching translation into English than non-English. |
Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text (2026.acl-long)
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| Challenge: | Code-switching (CSW) is widespread in multilingual communities and increasingly prevalent in online content. |
| Approach: | They propose a pipeline for producing linguistically grounded CSW variants of established benchmarks across five typologically diverse languages. |
| Outcome: | The proposed model sets show that inserting non-English tokens into English reduces accuracy on comprehension and reasoning benchmarks, whereas embedding English into non- English contexts often improves it. |
Beyond Monolingual Assumptions: A Survey on Code-Switched NLP in the Era of Large Language Models across Modalities (2026.acl-long)
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| Challenge: | Amidst the rapid advances of large language models, most LLMs struggle with mixed-language inputs, limited Code-switching datasets, and evaluation biases. |
| Approach: | They propose a roadmap for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence. |
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EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching (2022.findings-emnlp)
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| Challenge: | Existing methods for CS use dictionaries or parallel sentences with word-alignment to generate CS data by randomly switching words in a sentence. |
| Approach: | They propose a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. |
| Outcome: | The proposed method captures fine-grained cross-lingual semantics without corrupting syntax. |
Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training (2025.findings-acl)
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Zhijun Wang, Jiahuan Li, Hao Zhou, Rongxiang Weng, Jingang Wang, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang
| Challenge: | Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data. |
| Approach: | They investigate the existence of code-switching in the pre-training corpus and categorize it into four types within two quadrants. |
| Outcome: | The proposed approach improves performance across benchmarks and representation space. |
How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)
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| Challenge: | Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
| Approach: | They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input . |
| Outcome: | The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
Minimal Pair-Based Evaluation of Code-Switching (2025.acl-long)
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| Challenge: | Existing methods do not have wide language coverage, fail to account for the diverse range of CS phenomena, or do not scale. |
| Approach: | They propose to use minimal pairs of CS to estimate the extent to which large language models (LLMs) use code-switching in the same way as bilinguals. |
| Outcome: | The proposed model assigns higher probability to the naturally occurring CS sentence than to the variant for every language pair. |
Code-Switched Language Identification is Harder Than You Think (2024.eacl-long)
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| Challenge: | Code switching (CS) is a common phenomenon in written and spoken communication, but is handled poorly by many NLP applications. |
| Approach: | They propose to use CS language identification for corpus building to make it more realistic by scaling it to more languages and considering models with simpler architectures for faster inference. |
| Outcome: | The proposed system is based on a sentence-level multi-label tagging problem and provides recommendations for future work. |
Code-Switching and Syntax: A Large-Scale Experiment (2025.findings-acl)
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| Challenge: | Existing theories of code-switching (CS) have been refuted in subsequent investigations. |
| Approach: | They propose to use syntactic information to predict where bilinguals switch languages . they find that syntax alone is sufficient for an automatic system to distinguish between sentences in minimal pairs of CS, to the same degree as bilingual humans. |
| Outcome: | The proposed model can explain why bilinguals switch languages more often than in others, but there is no large-scale, multi-language, cross-phenomena experiment that tests this claim. |
Analyzing the Role of Part-of-Speech in Code-Switching: A Corpus-Based Study (2024.findings-eacl)
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| Challenge: | Code-switching (CS) is a common linguistic phenomenon wherein speakers fluidly transition between languages in conversation. |
| Approach: | They propose to use a part-of-speech (POS)-based analysis of Spanish-English and Mandarin-English corpora to examine the propensity of bilinguals to engage in CS. |
| Outcome: | The findings confirm the existence of a statistically significant connection between POS and the likelihood of CS across language pairs, but show that it diminishes as tokens distance themselves from CS instances. |