“Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition

Huy Nghiem, Umang Gupta, Fred Morstatter


Abstract
The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet subtle distinct categories of offensive speech. In this work, we explore meta-learning approaches to leverage the diversity of offensive speech corpora to enhance their reliable and efficient detection. We propose a joint embedding architecture that incorporates the input’s label and definition for classification via Prototypical Network. Our model achieves at least 75% of the maximal F1-score while using less than 10% of the available training data across 4 datasets. Our experimental findings also provide a case study of training strategies valuable to combat resource scarcity.
Anthology ID:
2024.eacl-long.78
Volume:
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1293–1309
Language:
URL:
https://aclanthology.org/2024.eacl-long.78
DOI:
Bibkey:
Cite (ACL):
Huy Nghiem, Umang Gupta, and Fred Morstatter. 2024. “Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1293–1309, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
“Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition (Nghiem et al., EACL 2024)
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PDF:
https://aclanthology.org/2024.eacl-long.78.pdf
Video:
 https://aclanthology.org/2024.eacl-long.78.mp4