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    <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.</abstract>
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%0 Conference Proceedings
%T A Novel Hierarchical BERT Architecture for Sarcasm Detection
%A Srivastava, Himani
%A Varshney, Vaibhav
%A Kumari, Surabhi
%A Srivastava, Saurabh
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F srivastava-etal-2020-novel
%X 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.
%R 10.18653/v1/2020.figlang-1.14
%U https://aclanthology.org/2020.figlang-1.14
%U https://doi.org/10.18653/v1/2020.figlang-1.14
%P 93-97

Markdown (Informal)

[A Novel Hierarchical BERT Architecture for Sarcasm Detection](https://aclanthology.org/2020.figlang-1.14) (Srivastava et al., Fig-Lang 2020)

• A Novel Hierarchical BERT Architecture for Sarcasm Detection (Srivastava et al., Fig-Lang 2020)

ACL

• Himani Srivastava, Vaibhav Varshney, Surabhi Kumari, and Saurabh Srivastava. 2020. A Novel Hierarchical BERT Architecture for Sarcasm Detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 93–97, Online. Association for Computational Linguistics.