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Recurrent synchronization network for emotion-cause pair extraction
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AI
DOI：10.1016/j.knosys.2021.107965.png)
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摘要
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Emotion-cause pair extraction is a fundamental task in emotion analysis, which aims to extract emotions and their corresponding causes from documents. Recent studies have employed two auxiliary tasks, namely emotion extraction and cause extraction, to facilitate the detection of emotion-cause pairs and their joint resolution in a multi-task learning framework. However, the implicit information-sharing mechanism in existing methods fails to fully utilize the rich interactive relations among these three tasks. In this study, we propose a recurrent synchronization network that explicitly models the interaction among different tasks. Our model performs multiple rounds of inference to detect emotions, causes, and emotion-cause pairs iteratively. Following each round of inference, the information from different modules is synchronized through explicit information transmission, allowing the three tasks to collaborate effectively. Extensive experiments demonstrate that our model can extract emotion-cause pairs more accurately, while significantly outperforming existing methods.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Emotion analysis
Neural network
Multi-task learning
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
Extracting Emotion Causes Using Learning to Rank Methods From an Information Retrieval Perspective.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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