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

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arxiv 2109.13711 v1 pith:OM2ZQBNA submitted 2021-09-28 cs.CL

Mehar Bhatia , Tenzin Singhay Bhotia , Akshat Agarwal , Prakash Ramesh , Shubham Gupta , Kumar Shridhar , Felix Laumann , Ayushman Dash This is my paper · ORCID
classification cs.CL
keywords hatelanguagesspeechdetectionlanguagemodelsmultilingualoffensive
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages

    cs.CL 2024-11 conditional novelty 3.0 of 10

    A survey cataloging datasets, features, and machine-learning methods for automatic hate speech detection in low-resource languages, organized by world region, with an overview of open challenges.

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