One to rule them all: Towards Joint Indic Language Hate Speech Detection

• URL: http://arxiv.org/abs/2109.13711v1
• Date: Tue, 28 Sep 2021 13:30:00 GMT
• Title: One to rule them all: Towards Joint Indic Language Hate Speech Detection
• Authors: Mehar Bhatia, Tenzin Singhay Bhotia, Akshat Agarwal, Prakash Ramesh, Shubham Gupta, Kumar Shridhar, Felix Laumann and Ayushman Dash
• Abstract summary: We present a multilingual architecture using state-of-the-art transformer language models to jointly learn hate and offensive speech detection. On the provided testing corpora, we achieve Macro F1 scores of 0.7996, 0.7748, 0.8651 for sub-task 1A and 0.6268, 0.5603 during the fine-grained classification of sub-task 1B.
• Score: 7.296361860015606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task. Social media today is a hotbed of toxic and hateful conversations, in various languages. Recent news reports have shown that current models struggle to automatically identify hate posted in minority languages. Therefore, efficiently curbing hate speech is a critical challenge and problem of interest. We present a multilingual architecture using state-of-the-art transformer language models to jointly learn hate and offensive speech detection across three languages namely, English, Hindi, and Marathi. On the provided testing corpora, we achieve Macro F1 scores of 0.7996, 0.7748, 0.8651 for sub-task 1A and 0.6268, 0.5603 during the fine-grained classification of sub-task 1B. These results show the efficacy of exploiting a multilingual training scheme.

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