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        <title>An improved Code-Switching Detection System for some Indic Languages</title>
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        <namePart type="given">Karan</namePart>
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            <title>Proceedings of the First Workshop on Multilingual Multicultural Evaluation</title>
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            <namePart type="given">Pinzhen</namePart>
            <namePart type="family">Chen</namePart>
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        <name type="personal">
            <namePart type="given">Vilém</namePart>
            <namePart type="family">Zouhar</namePart>
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            <namePart type="given">Hanxu</namePart>
            <namePart type="family">Hu</namePart>
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            <namePart type="given">Simran</namePart>
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            <namePart type="given">Wenhao</namePart>
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    <abstract>Code-switching is a common feature of multilingual communication, and identifying where the language switches reliably is essential for downstream tasks such as generating code-switched machine translations. This paper introduces CSDI, a Code-Switching Detection (CSD) system for Indic text, which jointly learns CSD, Named Entity Recognition, and Part-of-Speech tagging through a shared encoder. Leveraging multitask learning, CSDI captures linguistic cues that signal switching boundaries and achieves a new state-of-the-art macro-F1 score with near-zero ΔCMI across six Indic languages. The model also demonstrates strong cross-lingual transfer, effectively leveraging high-resource languages to improve low-resource performance. Despite challenges such as intra-word code-mixing and limited token-level context, CSDI establishes a new baseline for scalable, low-resource NLP research in code-mixed environments.</abstract>
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%0 Conference Proceedings
%T An improved Code-Switching Detection System for some Indic Languages
%A Bhanushali, Karan
%A Hohl, Fritz
%Y Chen, Pinzhen
%Y Zouhar, Vilém
%Y Hu, Hanxu
%Y Khanuja, Simran
%Y Zhu, Wenhao
%Y Haddow, Barry
%Y Birch, Alexandra
%Y Aji, Alham Fikri
%Y Sennrich, Rico
%Y Hooker, Sara
%S Proceedings of the First Workshop on Multilingual Multicultural Evaluation
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-368-5
%F bhanushali-hohl-2026-improved
%X Code-switching is a common feature of multilingual communication, and identifying where the language switches reliably is essential for downstream tasks such as generating code-switched machine translations. This paper introduces CSDI, a Code-Switching Detection (CSD) system for Indic text, which jointly learns CSD, Named Entity Recognition, and Part-of-Speech tagging through a shared encoder. Leveraging multitask learning, CSDI captures linguistic cues that signal switching boundaries and achieves a new state-of-the-art macro-F1 score with near-zero ΔCMI across six Indic languages. The model also demonstrates strong cross-lingual transfer, effectively leveraging high-resource languages to improve low-resource performance. Despite challenges such as intra-word code-mixing and limited token-level context, CSDI establishes a new baseline for scalable, low-resource NLP research in code-mixed environments.
%U https://aclanthology.org/2026.mme-main.3/
%P 35-48

Markdown (Informal)

[An improved Code-Switching Detection System for some Indic Languages](https://aclanthology.org/2026.mme-main.3/) (Bhanushali & Hohl, MME 2026)

• An improved Code-Switching Detection System for some Indic Languages (Bhanushali & Hohl, MME 2026)

ACL

• Karan Bhanushali and Fritz Hohl. 2026. An improved Code-Switching Detection System for some Indic Languages. In Proceedings of the First Workshop on Multilingual Multicultural Evaluation, pages 35–48, Rabat, Morocco. Association for Computational Linguistics.