COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework
This provides a tool for NLP researchers and practitioners working with multilingual datasets, though it is incremental as it builds on existing annotation frameworks.
The paper tackles the problem of annotating code-mixed multilingual text by introducing COMMENTATOR, a framework that achieved 5x faster annotations than the best baseline in evaluations on Hinglish text.
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. Our code is publicly available at \url{https://github.com/lingo-iitgn/commentator}. The demonstration video is available at \url{https://bit.ly/commentator_video}.