Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning

Botao Wang, Keke Tang, Peican Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterances in unannotated conversations, this task that has garnered increasing attention recently. Previous methods often apply Emotion-Cause Pair Extraction (ECPE) task models, treating the entire conversation as a whole for contextual interaction. However, statistical analysis shows that the number of emotion-cause pairs in ECPEC conversation data far exceeds that in ECPE datasets, leading to interference among multiple events within a conversation and causing noise to propagate between different events. To address this issue, we propose a novel CEnter eveNT-guided framEwoRk (CENTER). This model introduces a Center Event Detection task to construct a center event-aware graph that captures the unique representations of different event regions. Additionally, mimicking human reasoning processes, we build a center event reasoning graph and use graph neural network to facilitate the flow of information between utterance pairs, thereby uncovering the relationships between emotions and their causes. Experimental results demonstrate that our approach achieves state-of-the-art performance across three benchmark datasets.

Original languageEnglish
Title of host publicationEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024
EditorsYaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
PublisherAssociation for Computational Linguistics (ACL)
Pages10773-10783
Number of pages11
ISBN (Electronic)9798891761681
DOIs
StatePublished - 2024
Event2024 Findings of the Association for Computational Linguistics, EMNLP 2024 - Hybrid, Miami, United States
Duration: 12 Nov 202416 Nov 2024

Publication series

NameEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024

Conference

Conference2024 Findings of the Association for Computational Linguistics, EMNLP 2024
Country/TerritoryUnited States
CityHybrid, Miami
Period12/11/2416/11/24

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