Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction | |
Penghui Wei1,2; Jiahao Zhao1,2; Wenji Mao1,2 | |
2020-07 | |
会议名称 | The 58th Annual Meeting of the Association for Computational Linguistics |
会议日期 | 2020-7 |
会议地点 | Online |
出版者 | ACL |
摘要 | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44760 |
专题 | 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 |
通讯作者 | Wenji Mao |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Penghui Wei,Jiahao Zhao,Wenji Mao. Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction[C]:ACL,2020. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
[2020ACL] Effective (4038KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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