Summary of Commentator: a Code-mixed Multilingual Text Annotation Framework, by Rajvee Sheth et al.
COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework
by Rajvee Sheth, Shubh Nisar, Heenaben Prajapati, Himanshu Beniwal, Mayank Singh
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces COMMENTATOR, a code-mixed multilingual text annotation framework designed for annotating code-mixed text. The tool demonstrates its effectiveness in token-level and sentence-level language annotation tasks for Hinglish text. By comparing it to the best baseline, we show that COMMENTATOR leads to 5x faster annotations through robust qualitative human-based evaluations. The framework’s potential is showcased through publicly available code and a demonstration video. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to label mixed-language texts. Mixed-language texts are common in countries where many people speak multiple languages. To do this, the researchers created an annotation tool called COMMENTATOR. They tested it on Hinglish text, which is a mix of Hindi and English. The results showed that their tool made annotating 5 times faster than other methods. |
Keywords
* Artificial intelligence * Token
