mlsanityData to 14 Oct 2026

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

arXiv 2109.13711 ↗First posted 28 Sep 2021cs.CLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

11 of 29 references.

References · 11

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  10. 2016emoji2vec: Learning Emoji Representations from their Descriptioncited by 28
  11. 2021Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathicited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

7 papers.