Multiple Instance Learning for Offensive Language Detection

Jiexi Liu, Dehan Kong, Longtao Huang, Dinghui Mao, Hui Xue

Abstract
Automatic offensive language detection has become a crucial issue in recent years. Existing researches on this topic are usually based on a large amount of data annotated at sentence level to train a robust model. However, sentence-level annotations are expensive in practice as the scenario expands, while there exist a large amount of natural labels from historical information on online platforms such as reports and punishments. Notably, these natural labels are usually in bag-level corresponding to the whole documents (articles, user profiles, conversations, etc.). Therefore, we target at proposing an approach capable of utilizing the bag-level labeled data for offensive language detection in this study. For this purpose, we formalize this task into a multiple instance learning (MIL) problem. We break down the design of existing MIL methods and propose a hybrid fusion MIL model with mutual-attention mechanism. In order to verify the validity of the proposed method, we present two new bag-level labeled datasets for offensive language detection: OLID-bags and MINOR. Experimental results based on the proposed datasets demonstrate the effectiveness of the mutual-attention method at both sentence level and bag level.
Anthology ID:
2022.findings-emnlp.546
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7387–7396
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.546
DOI:
10.18653/v1/2022.findings-emnlp.546
Bibkey:
Cite (ACL):
Jiexi Liu, Dehan Kong, Longtao Huang, Dinghui Mao, and Hui Xue. 2022. Multiple Instance Learning for Offensive Language Detection. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7387–7396, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Multiple Instance Learning for Offensive Language Detection (Liu et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2022.findings-emnlp.546.pdf
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