A Multi-Task MRC Framework for Chinese Emotion Cause and Experiencer Extraction | |
Haoda Qian1,2; Qiudan Li1,2; Zaichuan Tang1,2 | |
2021-09 | |
会议日期 | 2021-09 |
会议地点 | Bratislava, Slovakia |
英文摘要 | 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. |
产权排序 | 1 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48595] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
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]. 见:. Bratislava, Slovakia. 2021-09. |
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