muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem
This addresses abusive content moderation for Indian social media platforms, but it is incremental as it builds on existing models with a specific ensemble approach.
The paper tackled abusive comment detection across 13 Indic languages on the Moj platform by proposing muBoost, an ensemble of CatBoost and MURIL models, achieving a mean F1-score of 89.286, an improvement over the baseline MURIL model's 87.48.
Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus, revenue on their platforms. In this paper, we are presenting our solution to Multilingual Abusive Comment Identification Problem on Moj, an Indian video-sharing social networking service, powered by ShareChat. The problem dealt with detecting abusive comments, in 13 regional Indic languages such as Hindi, Telugu, Kannada etc., on the videos on Moj platform. Our solution utilizes the novel muBoost, an ensemble of CatBoost classifier models and Multilingual Representations for Indian Languages (MURIL) model, to produce SOTA performance on Indic text classification tasks. We were able to achieve a mean F1-score of 89.286 on the test data, an improvement over baseline MURIL model with a F1-score of 87.48.