A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies
A. Seza Dogruöz, Sunayana Sitaram, Barbara E. Bullock, Almeida Jacqueline Toribio
摘要
The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-toend systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step towards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.
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引用它的顶会 Paper12
- Multilingual Large Language Models Are Not (Yet) Code-SwitchersRuochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, Genta Indra Winata 等EMNLP 2023 · 被引用 19 次
- Understanding and Mitigating Language Confusion in LLMsKelly Marchisio, Wei-Yin Ko, Alexandre Berard, Théo Dehaze 等EMNLP 2024 · 被引用 10 次
- Lost in the Mix: Evaluating LLM Understanding of Code-Switched TextAmr Mohamed, Yang Zhang, Michalis Vazirgiannis, Guokan ShangACL 2026 · 被引用 9 次
- Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive SurveyChih-Kai Yang, Neo S. Ho, Hung-yi LeeEMNLP 2025 · 被引用 7 次
- Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-SwitchingAlissa Ostapenko, Shuly Wintner, Melinda Fricke, Yulia TsvetkovACL 2022 · 被引用 6 次
它引用的顶会 Paper3
- Unsupervised Cross-lingual Representation Learning at ScaleAlexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary 等ACL 2020 · 被引用 539 次
- Do Multilingual Users Prefer Chat-bots that Code-mix? Let's Nudge and Find Out!Anshul Bawa, Pranav Khadpe, Pratik Joshi, Kalika Bali 等CSCW 2020 · 被引用 41 次
- GLUECoS: An Evaluation Benchmark for Code-Switched NLPSimran Khanuja, Sandipan Dandapat, Anirudh Srinivasan, Sunayana Sitaram 等ACL 2020 · 被引用 12 次
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