A Multi-Task MRC Framework for Chinese Emotion Cause and Experiencer Extraction
Haoda Qian1,2; Qiudan Li1,2; Zaichuan Tang1,2
2021-09
Conference NameICANN 2021: 30th International Conference on Artificial Neural Networks
Conference Date2021-09
Conference PlaceBratislava, Slovakia
Contribution Rank1
Abstract

Extracting emotion cause and experiencer from text can help people better understand users’ behavior patterns behind expressed emotions. Machine reading comprehension framework explicitly introduces a task-oriented query to boost the extraction task. In practice, how to learn a good task-oriented representation, accurately locate the boundary, and extract multiple causes and experiencers are the key technical challenges. To solve the above problems, this paper proposes BERT-based Machine Reading Comprehension Extraction Model with Multi-Task Learning (BERT-MRC-MTL). It first introduces query as prior knowledge and obtains text representation via BERT. Then, boundary-based and tag-based strategies are designed to select characters to be extracted, so as to extract multiple causes or experiencers simultaneously. Finally, hierarchical multi-task learning structure with residual connection is adopted to combine the answer extraction strategies. We conduct experiments on two public Chinese emotion datasets, and the results demonstrate the efficacy of our proposed model.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48595
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Haoda Qian,Qiudan Li,Zaichuan Tang. A Multi-Task MRC Framework for Chinese Emotion Cause and Experiencer Extraction[C],2021.
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