@inproceedings{bhanushali-hohl-2026-improved,
    title = "An improved Code-Switching Detection System for some {I}ndic Languages",
    author = "Bhanushali, Karan  and
      Hohl, Fritz",
    editor = "Chen, Pinzhen  and
      Zouhar, Vil{\'e}m  and
      Hu, Hanxu  and
      Khanuja, Simran  and
      Zhu, Wenhao  and
      Haddow, Barry  and
      Birch, Alexandra  and
      Aji, Alham Fikri  and
      Sennrich, Rico  and
      Hooker, Sara",
    booktitle = "Proceedings of the First Workshop on Multilingual Multicultural Evaluation",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.mme-main.3/",
    pages = "35--48",
    ISBN = "979-8-89176-368-5",
    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 $\Delta$CMI 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."
}