@inproceedings{sheth-etal-2024-commentator,
    title = "Commentator: A Code-mixed Multilingual Text Annotation Framework",
    author = "Sheth, Rajvee  and
      Nisar, Shubh  and
      Prajapati, Heenaben  and
      Beniwal, Himanshu  and
      Singh, Mayank",
    editor = "Hernandez Farias, Delia Irazu  and
      Hope, Tom  and
      Li, Manling",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-demo.11/",
    doi = "10.18653/v1/2024.emnlp-demo.11",
    pages = "101--109",
    abstract = "As the NLP community increasingly addresses challenges associated with multilingualism, robust annotation tools are essential to handle multilingual datasets efficiently. In this paper, we introduce a code-mixed multilingual text annotation framework, COMMENTATOR, specifically designed for annotating code- mixed text. The tool demonstrates its effectiveness in token-level and sentence-level language annotation tasks for Hinglish text. We perform robust qualitative human-based evaluations to showcase COMMENTATOR led to 5x faster annotations than the best baseline."
}