A Novel Hierarchical BERT Architecture for Sarcasm Detection

Himani Srivastava, Vaibhav Varshney, Surabhi Kumari, Saurabh Srivastava


Abstract
Online discussion platforms are often flooded with opinions from users across the world on a variety of topics. Many such posts, comments, or utterances are often sarcastic in nature, i.e., the actual intent is hidden in the sentence and is different from its literal meaning, making the detection of such utterances challenging without additional context. In this paper, we propose a novel deep learning-based approach to detect whether an utterance is sarcastic or non-sarcastic by utilizing the given contexts ina hierarchical manner. We have used datasets from two online discussion platforms - Twitter and Reddit1for our experiments. Experimental and error analysis shows that the hierarchical models can make full use of history to obtain a better representation of contexts and thus, in turn, can outperform their sequential counterparts.
Anthology ID:
2020.figlang-1.14
Volume:
Proceedings of the Second Workshop on Figurative Language Processing
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | Fig-Lang | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
93–97
Language:
URL:
https://www.aclweb.org/anthology/2020.figlang-1.14
DOI:
10.18653/v1/2020.figlang-1.14
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.figlang-1.14.pdf
Video:
 http://slideslive.com/38929704