An Analysis of Transformer-based Models for Code-mixed Conversational Hate-speech Identification

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Conference Article

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CEUR Workshop Proceedings


The current surge in social media usage has resulted in the widespread availability of harmful and hateful content. Such inflammatory content identification in social media is a crucial NLP problem. Recent research has repeatedly demonstrated that context-level semantics matter more than word-level semantics for assessing the existence of hate content. This paper investigates many state-of-the-art transformer-based models for hate content detection in code-mixed datasets. We emphasize transformer-based models since they capture context-level semantics. In particular, we concentrate on Google-MuRIL, XLM-Roberta-base, and Indic-BERT. Additionally, we have experimented with an ensemble of the three mentioned models. Based on substantial empirical evidence, we observe that Google-MuRIL emerges as the top model with macro F1-scores of 0.708 and 0.445 for HASOC shared tasks 1 and 2, placing us 1st and 6tℎ on the overall leaderboard standings respectively.

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