EMNLP 2024system demonstrations2 citations
Commentator: A Code-mixed Multilingual Text Annotation Framework
Rajvee Sheth, Shubh Nisar, Heenaben Prajapati, Himanshu Beniwal, Mayank Singh
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.
BibTeX
@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://aclanthology.org/2024.emnlp-demo.11/",
doi = "10.18653/v1/2024.emnlp-demo.11",
pages = "101--109"
}