@inproceedings{nghiem-etal-2024-define,
title = "{``}Define Your Terms{''} : Enhancing Efficient Offensive Speech Classification with Definition",
author = "Nghiem, Huy and
Gupta, Umang and
Morstatter, Fred",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.78",
pages = "1293--1309",
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.",
}
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%0 Conference Proceedings
%T “Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition
%A Nghiem, Huy
%A Gupta, Umang
%A Morstatter, Fred
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F nghiem-etal-2024-define
%X 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.
%U https://aclanthology.org/2024.eacl-long.78
%P 1293-1309
Markdown (Informal)
[“Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition](https://aclanthology.org/2024.eacl-long.78) (Nghiem et al., EACL 2024)
ACL