Summary of Overview Of the 2023 Icon Shared Task on Gendered Abuse Detection in Indic Languages, by Aatman Vaidya et al.
Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages
by Aatman Vaidya, Arnav Arora, Aditya Joshi, Tarunima Prabhakar
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 research paper presents the findings from the ICON 2023 shared task on Gendered Abuse Detection in Indic Languages. The task involves detecting gendered abuse in online text, using a novel dataset comprising approximately 6500 posts sourced from Twitter, split into train and test sets. The participants were given three subtasks to solve, with the best F-1 scores ranging from 0.572 to 0.616. This paper provides examples of hateful content, highlighting the importance of addressing gendered abuse online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about finding ways to detect online hate speech in Indian languages like Hindi and Tamil. The goal is to make the internet a safer place by identifying when someone is being bullied or abused because of their gender. To do this, researchers used Twitter posts as training data and tested different methods on a smaller set of new posts. Nine teams took part in the challenge, and some did much better than others at spotting hate speech. |
