TY - GEN
T1 - Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning
AU - Wang, Botao
AU - Tang, Keke
AU - Zhu, Peican
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85217622911&partnerID=8YFLogxK
U2 - 10.18653/v1/2024.findings-emnlp.632
DO - 10.18653/v1/2024.findings-emnlp.632
M3 - 会议稿件
AN - SCOPUS:85217622911
T3 - EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024
SP - 10773
EP - 10783
BT - EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024
A2 - Al-Onaizan, Yaser
A2 - Bansal, Mohit
A2 - Chen, Yun-Nung
PB - Association for Computational Linguistics (ACL)
T2 - 2024 Findings of the Association for Computational Linguistics, EMNLP 2024
Y2 - 12 November 2024 through 16 November 2024
ER -