Knowledge Commons of Institute of Automation,CAS
Towards Causal Explanation Detection with Pyramid Salient-Aware Network | |
Xinyu Zuo1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2 | |
2020 | |
会议名称 | Proceedings of the 19th China National Conference on Computational Linguistics |
会议日期 | October 30 - Novermber 1, 2020 |
会议地点 | Hainan, China (Online) |
摘要 | Causal explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect causal explanations on messages. PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on the Causal Explanation Detection task. |
学科门类 | 工学 ; 工学::计算机科学与技术(可授工学、理学学位) |
URL | 查看原文 |
收录类别 | EI |
资助项目 | Open Projects Program of National Laboratory of Pattern Recognition ; CCF-Tencent Open Fund ; National Natural Science Foundation of China[61806201] ; National Natural Science Foundation of China[61533018] |
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/173211/44830 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xinyu Zuo,Yubo Chen,Kang Liu,et al. Towards Causal Explanation Detection with Pyramid Salient-Aware Network[C],2020. |
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