A deep-learning framework to detect sarcasm targets

Jasabanta Patro, Srijan Bansal, Animesh Mukherjee


Abstract
In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets.We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.
Anthology ID:
D19-1663
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6336–6342
Language:
URL:
https://aclanthology.org/D19-1663
DOI:
10.18653/v1/D19-1663
Bibkey:
Cite (ACL):
Jasabanta Patro, Srijan Bansal, and Animesh Mukherjee. 2019. A deep-learning framework to detect sarcasm targets. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6336–6342, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A deep-learning framework to detect sarcasm targets (Patro et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1663.pdf