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2024 | OriginalPaper | Chapter

LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection

Authors : Kaiyue Wang, Fan Yang, Yucheng Yao, Xiabing Zhou

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

Malevolent Dialogue Response Detection has gained much attention from the NLP community recently. Existing methods have difficulties in effectively utilizing the conversational context and the malevolent information. In this work, we propose a novel framework, the Label Enhanced Multi-task learning (LEMT), which incorporates a structured representation of malevolence description information and exploits malevolence shift detection as an auxiliary task. Specifically, we introduce a hierarchical structure encoder based on prior probability knowledge to capture the semantic information of different malevolent types and integrate it with utterance information. In addition, the malevolence shift detection is modeled to improve the ability of the model to distinguish between different malevolent information. Experimental results show that our LEMT outperforms state-of-the-art methods and verifies the effectiveness of the modules.

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Footnotes
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Metadata
Title
LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection
Authors
Kaiyue Wang
Fan Yang
Yucheng Yao
Xiabing Zhou
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-41-9

Electronic ISBN: 978-981-9722-42-6

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2242-6_11

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