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China National Conference on Chinese Computational Linguistics

CCL 2021: Chinese Computational Linguistics pp 77–93Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12869))

Abstract

Emotion cause analysis (ECA) aims to identify the potential causes behind certain emotions in text. 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 problem, in 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 each word in the clause but also the relation between each word and emotion expression. We observe that ECK task can incorporate the contextual information from the ECC task, while ECC task can be improved by learning the correlation between emotion cause keywords and emotion from the ECK task. To fulfill the goal of joint learning, we propose a multi-head attention based multi-task learning method which utilizes a series of mechanisms including shared and private feature extractor, multi-head attention, emotion attention and label embedding to capture features and 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.

Supported by National Key R&D Program of China (2018YFB1004700), National Natural Science Foundation of China (61772122, 61872074).

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Notes

  1. 1.

    Each instance in the ECA corpus contains presumably a unique emotion and at least one emotion cause clause. A clause is typically a text segment separated by punctuation marks (e.g., ‘,’, ‘.’, ‘?’, ‘!’, etc.) in the given document.

  2. 2.

    http://hlt.hitsz.edu.cn/?page%20id=694.

  3. 3.

    https://github.com/fxsjy/jieba.

  4. 4.

    https://storage.googleapis.com/bert_models/2018_11_03/chinese_L12_H768_A12.zip.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang, 110207, China

    Xiangju Li, Shi Feng, Yifei Zhang & Daling Wang

Authors
  1. Xiangju Li
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  2. Shi Feng
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  3. Yifei Zhang
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  4. Daling Wang
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Corresponding author

Correspondence to Daling Wang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Sheng Li

  2. Tsinghua University, Beijing, China

    Maosong Sun

  3. Tsinghua University, Beijing, China

    Yang Liu

  4. Baidu (China), Beijing, China

    Hua Wu

  5. Chinese Academy of Sciences, Beijing, China

    Liu Kang

  6. Harbin Institute of Technology, Harbin, China

    Wanxiang Che

  7. Chinese Academy of Sciences, Beijing, China

    Shizhu He

  8. Beijing Language and Culture University, Beijing, China

    Gaoqi Rao

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Li, X., Feng, S., Zhang, Y., Wang, D. (2021). Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning. In: Li, S., et al. Chinese Computational Linguistics. CCL 2021. Lecture Notes in Computer Science(), vol 12869. Springer, Cham. https://doi.org/10.1007/978-3-030-84186-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-84186-7_6

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