A Multi-Task MRC Framework for Chinese Emotion Cause and Experiencer Extraction
Haoda Qian1,2; Qiudan Li1,2; Zaichuan Tang1,2
2021-09
会议名称ICANN 2021: 30th International Conference on Artificial Neural Networks
会议日期2021-09
会议地点Bratislava, Slovakia
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摘要

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.

文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/48595
专题多模态人工智能系统全国重点实验室_互联网大数据与信息安全
作者单位1.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
第一作者单位中国科学院自动化研究所
推荐引用方式
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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