@inproceedings{n-p-etal-2024-multi,
title = "Multi-Task Learning for Faux-Hate Detection in {H}indi-{E}nglish Code-Mixed Text",
author = "N P, Hitesh and
Ankith, D and
A N, Poornachandra and
C B, Abhilash",
editor = "Biradar, Shankar and
Reddy, Kasu Sai Kartheek and
Saumya, Sunil and
Akhtar, Md. Shad",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-fauxhate.10/",
pages = "50--55",
abstract = "The prevalence of harmful internet content is on the rise, especially among young people. Thismakes social media sites breeding grounds forhate speech and negativity even though theirpurpose is to create connections. The study pro-poses a multi-task learning model for the iden-tification and analysis of harmful social mediacontent. This classifies the text into fake/realand hate/non-hate categories and further identi-fies the target and severity of the harmful con-tent. The proposed model showed significantimprovements in performance with training ontransliterated data as compared to code-mixeddata. It ranked 2nd and 3rd in the ICON 2024Faux-Hate Shared Task and the performanceshave made it very effective against harmful con-tent."
}
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<abstract>The prevalence of harmful internet content is on the rise, especially among young people. Thismakes social media sites breeding grounds forhate speech and negativity even though theirpurpose is to create connections. The study pro-poses a multi-task learning model for the iden-tification and analysis of harmful social mediacontent. This classifies the text into fake/realand hate/non-hate categories and further identi-fies the target and severity of the harmful con-tent. The proposed model showed significantimprovements in performance with training ontransliterated data as compared to code-mixeddata. It ranked 2nd and 3rd in the ICON 2024Faux-Hate Shared Task and the performanceshave made it very effective against harmful con-tent.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning for Faux-Hate Detection in Hindi-English Code-Mixed Text
%A N P, Hitesh
%A Ankith, D.
%A A N, Poornachandra
%A C B, Abhilash
%Y Biradar, Shankar
%Y Reddy, Kasu Sai Kartheek
%Y Saumya, Sunil
%Y Akhtar, Md. Shad
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F n-p-etal-2024-multi
%X The prevalence of harmful internet content is on the rise, especially among young people. Thismakes social media sites breeding grounds forhate speech and negativity even though theirpurpose is to create connections. The study pro-poses a multi-task learning model for the iden-tification and analysis of harmful social mediacontent. This classifies the text into fake/realand hate/non-hate categories and further identi-fies the target and severity of the harmful con-tent. The proposed model showed significantimprovements in performance with training ontransliterated data as compared to code-mixeddata. It ranked 2nd and 3rd in the ICON 2024Faux-Hate Shared Task and the performanceshave made it very effective against harmful con-tent.
%U https://aclanthology.org/2024.icon-fauxhate.10/
%P 50-55
Markdown (Informal)
[Multi-Task Learning for Faux-Hate Detection in Hindi-English Code-Mixed Text](https://aclanthology.org/2024.icon-fauxhate.10/) (N P et al., ICON 2024)
ACL
- Hitesh N P, D Ankith, Poornachandra A N, and Abhilash C B. 2024. Multi-Task Learning for Faux-Hate Detection in Hindi-English Code-Mixed Text. In Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate), pages 50–55, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).