@inproceedings{basu-roy-chowdhury-chaturvedi-2021-commonsense,
title = "Does Commonsense help in detecting Sarcasm?",
author = "Basu Roy Chowdhury, Somnath and
Chaturvedi, Snigdha",
editor = "Sedoc, Jo{\~a}o and
Rogers, Anna and
Rumshisky, Anna and
Tafreshi, Shabnam",
booktitle = "Proceedings of the Second Workshop on Insights from Negative Results in NLP",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.insights-1.2/",
doi = "10.18653/v1/2021.insights-1.2",
pages = "9--15",
abstract = "Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a deep understanding of language, context, and world knowledge. In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection. For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input. Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model. We perform an exhaustive set of experiments to analyze where commonsense support adds value and where it hurts classification. Our implementation is publicly available at: \url{https://github.com/brcsomnath/commonsense-sarcasm}."
}
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%0 Conference Proceedings
%T Does Commonsense help in detecting Sarcasm?
%A Basu Roy Chowdhury, Somnath
%A Chaturvedi, Snigdha
%Y Sedoc, João
%Y Rogers, Anna
%Y Rumshisky, Anna
%Y Tafreshi, Shabnam
%S Proceedings of the Second Workshop on Insights from Negative Results in NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F basu-roy-chowdhury-chaturvedi-2021-commonsense
%X Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a deep understanding of language, context, and world knowledge. In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection. For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input. Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model. We perform an exhaustive set of experiments to analyze where commonsense support adds value and where it hurts classification. Our implementation is publicly available at: https://github.com/brcsomnath/commonsense-sarcasm.
%R 10.18653/v1/2021.insights-1.2
%U https://aclanthology.org/2021.insights-1.2/
%U https://doi.org/10.18653/v1/2021.insights-1.2
%P 9-15
Markdown (Informal)
[Does Commonsense help in detecting Sarcasm?](https://aclanthology.org/2021.insights-1.2/) (Basu Roy Chowdhury & Chaturvedi, insights 2021)
ACL
- Somnath Basu Roy Chowdhury and Snigdha Chaturvedi. 2021. Does Commonsense help in detecting Sarcasm?. In Proceedings of the Second Workshop on Insights from Negative Results in NLP, pages 9–15, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.