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11 November 2021 An end-to-end multi-task learning to link framework for emotion-cause pair extraction
Haolin Song, Dawei Song
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Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 1207604 (2021) https://doi.org/10.1117/12.2607175
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China
Abstract
Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. To solve ECPE task, we regards emotion-cause pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. We propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches.
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Haolin Song and Dawei Song "An end-to-end multi-task learning to link framework for emotion-cause pair extraction", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 1207604 (11 November 2021); https://doi.org/10.1117/12.2607175
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KEYWORDS
Computer programming
Performance modeling
Matrices
Modeling
Data modeling
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