Papers with code-switching
Processing and Understanding Mixed Language Data (D19-2)
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| Challenge: | Multilingual communities exhibit code-mixing, mixing of two or more languages in a single conversation . social media and other informal interactive platforms are allowing code-switching in user-generated text . |
| Approach: | a tutorial aims to provide a foundation for researchers to study code-mixing in multilingual communities. |
| Outcome: | a tutorial aims to provide new researchers with a foundation in linguistics and computational aspects of code-mixing. |
HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition (2026.findings-eacl)
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| Challenge: | Recent advances in automatic speech recognition (ASR) have pushed error rates below 5% on standard monolingual benchmarks. |
| Approach: | They propose a framework for the evaluation of multilingual ASR models using loanword labels and a hierarchical CS-level labeling scheme that allows for fine-tuning with synthetic CS data. |
| Outcome: | The proposed framework provides a means for the precise evaluation of multilingual ASR models and fosters research in the field. |
MaskLID: Code-Switching Language Identification through Iterative Masking (2024.acl-short)
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| Challenge: | Sentence-level LIDs are classifiers trained on monolingual texts to provide single labels, typically using a softmax layer to turn scores into probabilities. |
| Approach: | They propose a simple yet effective code-switching language identification method that uses the LID itself to mask features associated with L1 and L2 in the next round. |
| Outcome: | The proposed method is based on two open-source LIDs based in the FastText architecture and does not require any external resources. |
A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies (2021.acl-long)
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| Challenge: | linguistic and social aspects of code-switching are not discussed in the literature in linguistics. |
| Approach: | They propose to examine linguistic and social aspects of code-switching across a wide range of languages in a survey of the literature in linguistics and language technologies. |
| Outcome: | The proposed framework aims to increase the clarity and depth of computational investigations of C-S and bridge the fields so that they might be mutually reinforcing. |
Measuring Entrainment in Spontaneous Code-switched Speech (2024.naacl-long)
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| Challenge: | Existing studies of entrainment in code-switched domains have been limited to human-machine textual interactions. |
| Approach: | They propose to use acoustic-prosodic features to identify multiple dimensions and feature sets of entrainment in code-switched speech. |
| Outcome: | The findings give rise to important implications for the potentially “universal” nature of entrainment as a communication phenomenon and potential applications in inclusive and interactive speech technology. |
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data (2023.findings-acl)
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| Challenge: | Using zero-shot rankers, cross-lingual IR models are limited by their language coverage. |
| Approach: | They propose to train ranking models on artificially code-switched data instead of using a dictionary. |
| Outcome: | The proposed approach is robust towards the ratio of code-switched tokens and extends to unseen languages. |
Lexical Normalization for Code-switched Data and its Effect on POS Tagging (2021.eacl-main)
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| Challenge: | Social media data can be used to improve natural language processing performance, but it is often overlooked by lexical normalization systems. |
| Approach: | They propose three lexical normalization models specifically designed to handle code-switched data and evaluate their performance on POS tags. |
| Outcome: | The proposed models outperform monolingual models and lead to 5.4% performance increase for POS tagging compared to unnormalized input. |
Subword-Level Language Identification for Intra-Word Code-Switching (N19-1)
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| Challenge: | Code-switching (CS) is a phenomenon of alternating between two or more languages in conversations . if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language. |
| Approach: | They propose to extend the language identification task to the subword level by splitting mixed words while tagging each part with a language ID. |
| Outcome: | The proposed model outperforms the baseline on a Spanish–Wixarika and adapted German–Turkish datasets. |
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text (2023.eacl-main)
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| Challenge: | Code-switching (CS) is a problem in machine translation, but its performance is not investigated for CS settings. |
| Approach: | They propose to use morphological segmentation techniques for machine translation tasks . they compare morphology-based and frequency-based segmentation for MT tasks based on data size . |
| Outcome: | The proposed approach performs best in MT tasks but under-performs in other languages. |
Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching (2022.acl-long)
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| Challenge: | Prior approaches for predicting code-switching only consider shallow linguistic context. |
| Approach: | They hypothesize that enriching models with speaker information can guide them to pick up on relevant inductive biases. |
| Outcome: | The proposed model improves on a speaker-driven task in English–Spanish bilingual dialogues by adding sociolinguistically-grounded speaker features as prepended prompts. |
Code-Switched Text Synthesis in Unseen Language Pairs (2023.findings-acl)
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| Challenge: | Existing efforts on text synthesis for code-switching require training on code-witched texts in the target language pairs. |
| Approach: | They propose a model that synthesizes code-switched texts for language pairs absent from training data by adding an additional code-sharing module to a pre-trained machine translation model. |
| Outcome: | The proposed model synthesizes code-switched texts for language pairs lacking from training data. |
GLUECoS: An Evaluation Benchmark for Code-Switched NLP (2020.acl-main)
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| Challenge: | Recent studies show multilingual contextual embedding models perform better on cross-lingual and multilingual tasks. |
| Approach: | They propose to evaluate multilingual contextual embedding models on multilingual data . they use language identification from text, POS tagging, Named Entity Recognition and Question Answering . |
| Outcome: | The proposed benchmark evaluates models on language identification from text, POS tagging, Named Entity Recognition, Question Answering and a new task for code-switching, Natural Language Inference. |
Surprisal Predicts Code-Switching in Chinese-English Bilingual Text (2020.emnlp-main)
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| Challenge: | a new study examines the propensity of bilinguals to switch languages . word surprisal and word entropy are important predictors of code-switching . |
| Approach: | They propose high cognitive effort as a reason for code-switching . they use a computational model of surprisal and word entropy to model code-changing . |
| Outcome: | The proposed model shows that word surprisal, but not entropy, is a significant predictor . sentence length is also a predictor, which has been related to sentence complexity . |
Meta-Transfer Learning for Code-Switched Speech Recognition (2020.acl-main)
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| Challenge: | Increasing number of people in the world today speak a mixed-language as a result of being multilingual. |
| Approach: | They propose a method to transfer learn on a code-switched speech recognition system by extracting information from high-resource monolingual datasets. |
| Outcome: | The proposed model outperforms baselines on speech recognition and language modeling tasks and is faster to converge. |
A Resource for Computational Experiments on Mapudungun (2020.lrec-1)
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Mingjun Duan, Carlos Fasola, Sai Krishna Rallabandi, Rodolfo Vega, Antonios Anastasopoulos, Lori Levin, Alan W Black
| Challenge: | Low-resource languages still lag behind in documenting endangered languages . a large corpus of culturally significant conversations is available for computational experiments . |
| Approach: | They propose a resource for computational experiments on Mapudungun, a polysynthetic indigenous language spoken in Chile. |
| Outcome: | The proposed corpus provides 142 hours of culturally significant conversations in Mapudungun . the language is spoken by the Mapuche people of southern Chile and western argentina . |
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition (D19-1)
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| Challenge: | Existing work on name-switching focuses on word-level aspects but neglects subword-level characteristics shared across languages. |
| Approach: | They propose hierarchical meta-Embeddings that combine word-level and subword-level embeddings to create language-agnostic lexical representations. |
| Outcome: | The proposed model achieves state-of-the-art in English-Spanish code-switching scenarios. |
How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
| Approach: | They propose a multilingual model pre-trained from monolingual Wikipedia corpora . they show that multilingual BERT is surprisingly good at zero-shot cross-lingual model transfer . |
| Outcome: | The proposed model can find translation pairs, but it exhibits systematic deficiencies affecting certain language pairs. |
GupShup: Summarizing Open-Domain Code-Switched Conversations (2021.emnlp-main)
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Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah
| Challenge: | Abstractive summarization is the process of generating a condensed version of a given conversation while preserving the most salient aspects. |
| Approach: | They propose to use a dataset to analyze code-switched conversations in Hindi and English to summarize them. |
| Outcome: | The proposed dataset contains over 6,800 code-switched conversations and their corresponding human-annotated summaries in English (En) and Hi-En. |
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. |
Multilingual Large Language Models Are Not (Yet) Code-Switchers (2023.emnlp-main)
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| Challenge: | Existing multilingual Large Language Models are not specifically trained with objectives for managing code-switching scenarios. |
| Approach: | They propose to use multilingual Large Language Models to perform sentiment analysis, machine translation, summarization and word-level language identification to compare their performance to fine-tuned models of much smaller scales. |
| Outcome: | The proposed models show that they underperform in comparison to fine-tuned models of much smaller scales. |
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. |
LLM-based Code-Switched Text Generation for Grammatical Error Correction (2024.emnlp-main)
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| Challenge: | Code-switching (CSW) is a part of multilingual conversation and is gaining popularity in social and professional settings. |
| Approach: | They propose to use synthetic data to generate a model capable of correcting grammatical errors in CSW texts. |
| Outcome: | The proposed model improves on existing systems on an authentic dataset from English as a second language learners. |
Detecting Propaganda Techniques in Code-Switched Social Media Text (2023.emnlp-main)
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| Challenge: | a new study aims to detect propaganda in multiple languages using code-switching . social media platforms have made it easier for anyone to spread information to a wide audience . |
| Approach: | They propose to detect propaganda techniques in code-switched texts using a corpus of 1,030 texts . they propose to model multilinguality directly rather than using translation . |
| Outcome: | The proposed method combines different languages within the same text, presenting a challenge for automatic systems. |
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. |
| Approach: | They propose to use a synthetic English-korean CS question-answering dataset to investigate this potential. |
| Outcome: | The proposed model can activate, identify and leverage knowledge for reasoning in low-resource languages. |