An Attention Based Multi-view Model for Sarcasm Cause Detection (Student Abstract)

Authors

  • Hejing Liu School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Qiudan Li Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China Shenzhen Artificial Intelligence and Data Science Institute (Longhua)
  • Zaichuan Tang School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Jie Bai Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China Shenzhen Artificial Intelligence and Data Science Institute (Longhua)

Keywords:

Sarcasm Cause Detection, Attention Mechanism, Multi-view Manner

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.

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Published

2021-05-18

How to Cite

Liu, H., Li, Q., Tang, Z., & Bai, J. (2021). An Attention Based Multi-view Model for Sarcasm Cause Detection (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15833-15834. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17913

Issue

Section

AAAI Student Abstract and Poster Program