Toxicity Detection for Indic Multilingual Social Media Content
This addresses the problem of identifying toxic content in code-mixed Indic languages for social media platforms, though it appears incremental as it builds on existing models and competition data.
The paper tackles toxicity detection in low-resource Indic languages on social media by developing an ensemble system using multilingual transformer models, achieving a Mean F-1 score of 0.9 and placing first on a competition leaderboard.
Toxic content is one of the most critical issues for social media platforms today. India alone had 518 million social media users in 2020. In order to provide a good experience to content creators and their audience, it is crucial to flag toxic comments and the users who post that. But the big challenge is identifying toxicity in low resource Indic languages because of the presence of multiple representations of the same text. Moreover, the posts/comments on social media do not adhere to a particular format, grammar or sentence structure; this makes the task of abuse detection even more challenging for multilingual social media platforms. This paper describes the system proposed by team 'Moj Masti' using the data provided by ShareChat/Moj in \emph{IIIT-D Multilingual Abusive Comment Identification} challenge. We focus on how we can leverage multilingual transformer based pre-trained and fine-tuned models to approach code-mixed/code-switched classification tasks. Our best performing system was an ensemble of XLM-RoBERTa and MuRIL which achieved a Mean F-1 score of 0.9 on the test data/leaderboard. We also observed an increase in the performance by adding transliterated data. Furthermore, using weak metadata, ensembling and some post-processing techniques boosted the performance of our system, thereby placing us 1st on the leaderboard.