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

Hejing Liu, Qiudan Li, Zaichuan Tang, Jie Bai

AAAI 2021 pp. 15833-15834

doi:10.1609/AAAI.V35I18.17913 /aaai/2021/liu2021aaai-attention/

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.

PDF AAAI Semantic Scholar

Cite

Text

Liu et al. "An Attention Based Multi-View Model for Sarcasm Cause Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17913

Markdown

[Liu et al. "An Attention Based Multi-View Model for Sarcasm Cause Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/liu2021aaai-attention/) doi:10.1609/AAAI.V35I18.17913

BibTeX

@inproceedings{liu2021aaai-attention,
  title     = {{An Attention Based Multi-View Model for Sarcasm Cause Detection (Student Abstract)}},
  author    = {Liu, Hejing and Li, Qiudan and Tang, Zaichuan and Bai, Jie},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15833-15834},
  doi       = {10.1609/AAAI.V35I18.17913},
  url       = {https://mlanthology.org/aaai/2021/liu2021aaai-attention/}
}