@inproceedings{xiangju-etal-2021-multi,
title = "Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning",
author = "Xiangju, Li and
Shi, Feng and
Yifei, Zhang and
Daling, Wang",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.83/",
pages = "928--939",
language = "eng",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning
%A Xiangju, Li
%A Shi, Feng
%A Yifei, Zhang
%A Daling, Wang
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G eng
%F xiangju-etal-2021-multi
%X 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.
%U https://aclanthology.org/2021.ccl-1.83/
%P 928-939
Markdown (Informal)
[Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning](https://aclanthology.org/2021.ccl-1.83/) (Xiangju et al., CCL 2021)
ACL