| Challenge: | Code-switching (CS) is a common phenomenon in real-world conversations and poses significant challenges for multilingual speech technology. |
| Approach: | They propose a pipeline for generating high-quality, natural CS samples without altering sentence semantics. |
| Outcome: | The proposed pipeline generates high-quality, natural CS samples without altering sentence semantics without alteration of sentence semantic. |
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End-to-End Speech Translation for Code Switched Speech (2022.findings-acl)
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Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
| Challenge: | Code switching (CS) is the phenomenon of interchangeably using words and phrases from different languages. |
| Approach: | They propose a new ST corpus that extends the joint transcription and translation setup. |
| Outcome: | The proposed model performs well even when no training data is used. |
CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units (2024.acl-srw)
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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. |
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UniVocal: Unified Speech-Singing Code-Switching Synthesis (2026.acl-long)
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| Challenge: | Existing systems cannot automatically determine when to switch between modes based on text content. |
| Approach: | They propose a unified framework that implicitly infers vocal modes from text context to pioneer SCS Synthesis. |
| Outcome: | The proposed framework infers vocal modes solely from text context to pioneer SCS Synthesis. |
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. |
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)
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| Challenge: | Code-switching (CS) is the alternation of languages within an utterance or conversation. |
| Approach: | They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available. |
| Outcome: | The proposed models improve when only English data is available alongside zero or a few CS training instances. |
Toward the Limitation of Code-Switching in Cross-Lingual Transfer (2022.emnlp-main)
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| Challenge: | Recent studies have shown the success of multilingual pretrained models for cross-lingual knowledge transfer. |
| Approach: | They propose to make code-switched sentences replace tokens from multiple languages so they are grammatically consistent . they also consider the similarity between context and the switched tokens to ensure that the newly substituted sentences are grammatically consistent - a limitation that could affect inference . |
| Outcome: | The proposed method outperforms the mBERT and original code-switching method on cross-lingual POS and Named-Entity-Recognition tasks on 30+ languages. |
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. |
Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling (2020.lrec-1)
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| Challenge: | Multilingual people code-mix using English phonetic typing and insertion of anglicisms in their native language. |
| Approach: | They propose to use minority positive sampling to selectively induce more sample to achieve better performance. |
| Outcome: | The proposed model performs better than other models, but switching points are the main challenge . |
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text (2021.acl-long)
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| Challenge: | a computational model for code-switching text is lacking in the corpus of real text. |
| Approach: | They propose a neural machine translation model to generate Hindi-English code-switched sentences using monolingual Hindi sentences. |
| Outcome: | The proposed model reduces perplexity on a language modeling task and improves on linguistic inference tasks. |
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. |
| Outcome: | The proposed frameworks are based on 327 studies spanning five research areas, 15+ NLP tasks, 30+ datasets, and 80+ languages. |