An Attention Based Multi-view Model for Sarcasm Cause Detection
Hejing Liu1,2; Qiudan Li2,3; Zaichuan Tang1,2; Jie Bai2,3
2021
Conference NameThe Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)
Conference Date2021-2-9
Conference Placevirtual conference
Abstract

Sarcasm often relates to people’s implicit discontent with certain products and policies. Existing research mainly focus on sarcasm detection, while the deep causal relationships in the full conversation remained unexplored. This paper formulates a novel research question of sarcasm cause detection, and proposes an attention based model that simultaneously captures different semantic associations as well as the inner causal logics in multi-view manner. Experiments on public Reddit dataset prove the efficacy of the proposed model.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44943
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
Corresponding AuthorQiudan Li
Affiliation1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.Shenzhen Artificial Intelligence and Data Science Institute (Longhua)
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hejing Liu,Qiudan Li,Zaichuan Tang,et al. An Attention Based Multi-view Model for Sarcasm Cause Detection[C],2021.
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