@inproceedings{bhaskar-etal-2024-decoding,
title = "Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head {R}o{BERT}a Model with Multi-Task Learning",
author = "Bhaskar, Yash and
Bahad, Sankalp and
Krishnamurthy, Parameswari",
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.3/",
pages = "12--15",
abstract = "Social media platforms, while enabling globalconnectivity, have become hubs for the rapidspread of harmful content, including hatespeech and fake narratives (Davidson et al.,2017; Shu et al., 2017). The Faux-Hateshared task focuses on detecting a specific phe-nomenon: the generation of hate speech drivenby fake narratives, termed Faux-Hate. Partici-pants are challenged to identify such instancesin code-mixed Hindi-English social media text.This paper describes our system developed forthe shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involv-ing fake and hate speech classification, and(b) Target and Severity prediction, categoriz-ing the intended target and severity of hate-ful content. Our approach combines advancednatural language processing techniques withdomain-specific pretraining to enhance perfor-mance across both tasks. The system achievedcompetitive results, demonstrating the efficacyof leveraging multi-task learning for this com-plex problem."
}
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<abstract>Social media platforms, while enabling globalconnectivity, have become hubs for the rapidspread of harmful content, including hatespeech and fake narratives (Davidson et al.,2017; Shu et al., 2017). The Faux-Hateshared task focuses on detecting a specific phe-nomenon: the generation of hate speech drivenby fake narratives, termed Faux-Hate. Partici-pants are challenged to identify such instancesin code-mixed Hindi-English social media text.This paper describes our system developed forthe shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involv-ing fake and hate speech classification, and(b) Target and Severity prediction, categoriz-ing the intended target and severity of hate-ful content. Our approach combines advancednatural language processing techniques withdomain-specific pretraining to enhance perfor-mance across both tasks. The system achievedcompetitive results, demonstrating the efficacyof leveraging multi-task learning for this com-plex problem.</abstract>
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%0 Conference Proceedings
%T Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head RoBERTa Model with Multi-Task Learning
%A Bhaskar, Yash
%A Bahad, Sankalp
%A Krishnamurthy, Parameswari
%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 bhaskar-etal-2024-decoding
%X Social media platforms, while enabling globalconnectivity, have become hubs for the rapidspread of harmful content, including hatespeech and fake narratives (Davidson et al.,2017; Shu et al., 2017). The Faux-Hateshared task focuses on detecting a specific phe-nomenon: the generation of hate speech drivenby fake narratives, termed Faux-Hate. Partici-pants are challenged to identify such instancesin code-mixed Hindi-English social media text.This paper describes our system developed forthe shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involv-ing fake and hate speech classification, and(b) Target and Severity prediction, categoriz-ing the intended target and severity of hate-ful content. Our approach combines advancednatural language processing techniques withdomain-specific pretraining to enhance perfor-mance across both tasks. The system achievedcompetitive results, demonstrating the efficacyof leveraging multi-task learning for this com-plex problem.
%U https://aclanthology.org/2024.icon-fauxhate.3/
%P 12-15
Markdown (Informal)
[Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head RoBERTa Model with Multi-Task Learning](https://aclanthology.org/2024.icon-fauxhate.3/) (Bhaskar et al., ICON 2024)
ACL