We Know An Emotion There, But What Type Is It and What Triggers It? Towards Emotion-Cause Triplet Extraction
Authors: 
Haolin Song, Dawei SongAuthors Info & Claims
Haolin Song, Dawei SongAuthors Info & ClaimsArticle No.: 91, Pages 1 - 6
Abstract
A recently emerging emotion analysis task is emotion-cause pair extraction (ECPE), which aims to simultaneously obtain emotions and their corresponding causes expressed in documents. While ECPE is appealing, it does not take into account the types of emotions when connecting them with causes. The emotion type can reflect affective states and subjective information being recognized. Without it, an emotion-cause pair is of limited usefulness in practical applications such as strategy formulation, decision making, etc. To address this issue, we propose a new task, named emotion-cause triplet extraction (ECTE). It not only extracts emotion-cause pairs but at the same time also distinguishes emotion types. Further, we establish a biaffine attention-based multi-task learning approach to the ECTE task. Experiments are carried out on a benchmarking emotion-cause corpus that is slightly modified to suit the ECTE task. The results demonstrate the feasibility of the new task and the effectiveness of our proposed approach.
References
[1]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473(2014).
[2]
Xinhong Chen, Qing Li, and Jianping Wang. 2020. A Unified Sequence Labeling Model for Emotion Cause Pair Extraction. In Proceedings of the 28th International Conference on Computational Linguistics. 208–218.
[3]
Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, and Xiaoqiang Zhang. 2020. End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network. In Proceedings of the 28th International Conference on Computational Linguistics. 198–207.
[4]
Ying Chen, Sophia Yat Mei Lee, Shoushan Li, and Chu-Ren Huang. 2010. Emotion cause detection with linguistic constructions. In Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, 179–187.
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).
[6]
Zixiang Ding, Rui Xia, and Jianfei Yu. 2020. ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3161–3170.
[7]
Timothy Dozat and Christopher D Manning. 2016. Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734(2016).
[8]
Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Min Yang, and Ruifeng Xu. 2020. Transition-based directed graph construction for emotion-cause pair extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3707–3717.
[9]
Lin Gui, Dongyin Wu, Ruifeng Xu, Qin Lu, and Yu Zhou. 2016. Event-Driven Emotion Cause Extraction with Corpus Construction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 1639–1649.
[10]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision. 1026–1034.
[11]
Sophia Yat Mei Lee, Ying Chen, and Chu-Ren Huang. 2010. A text-driven rule-based system for emotion cause detection. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Association for Computational Linguistics, 45–53.
[12]
Shen Li, Zhe Zhao, Renfen Hu, Wensi Li, Tao Liu, and Xiaoyong Du. 2018. Analogical Reasoning on Chinese Morphological and Semantic Relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Melbourne, Australia). Association for Computational Linguistics, 138–143. http://aclweb.org/anthology/P18-2023
[13]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013).
[14]
Shiv Naresh Shivhare and Saritha Khethawat. 2012. Emotion detection from text. arXiv preprint arXiv:1205.4944(2012).
[15]
Carlo Strapparava and Rada Mihalcea. 2008. Learning to identify emotions in text. In Proceedings of the 2008 ACM symposium on Applied computing. 1556–1560.
[16]
Penghui Wei, Jiahao Zhao, and Wenji Mao. 2020. Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3171–3181.
[17]
Rui Xia and Zixiang Ding. 2019. Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 1003–1012. https://doi.org/10.18653/v1/P19-1096
[18]
Chaofa Yuan, Chuang Fan, Jianzhu Bao, and Ruifeng Xu. 2020. Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 3568–3573.
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
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Association for Computing Machinery
New York, NY, United States
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Published: 25 February 2022
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- Refereed limited
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- Natural Science Foundation of China
- National Key Research and Development Program of China
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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