Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning

Li Xiangju, Feng Shi, Zhang Yifei, Wang Daling

Abstract
Emotion cause analysis (ECA) aims to identify the potential causes behind certain emotions intext. Lots of ECA models have been designed to extract the emotion cause at the clause level.However in many scenarios only extracting the cause clause is ambiguous. To ease the problemin this paper we introduce multi-level emotion cause analysis which focuses on identifying emotion cause clause (ECC) and emotion cause keywords (ECK) simultaneously. ECK is a more challenging task since it not only requires capturing the specific understanding of the role of eachword in the clause but also the relation between each word and emotion expression. We observethat ECK task can incorporate the contextual information from the ECC task while ECC taskcan be improved by learning the correlation between emotion cause keywords and emotion fromthe ECK task. To fulfill the goal of joint learning we propose a multi-head attention basedmulti-task learning method which utilizes a series of mechanisms including shared and privatefeature extractor multi-head attention emotion attention and label embedding to capture featuresand correlations between the two tasks. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods on a benchmark emotion cause dataset.
Anthology ID:
2021.ccl-1.83
Volume:
Proceedings of the 20th Chinese National Conference on Computational Linguistics
Month:
August
Year:
2021
Address:
Huhhot, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
928–939
Language:
English
URL:
https://aclanthology.org/2021.ccl-1.83
DOI:
Bibkey:
Cite (ACL):
Li Xiangju, Feng Shi, Zhang Yifei, and Wang Daling. 2021. Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 928–939, Huhhot, China. Chinese Information Processing Society of China.
Cite (Informal):
Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning (Xiangju et al., CCL 2021)
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