Does Commonsense help in detecting Sarcasm?

Somnath Basu Roy Chowdhury, Snigdha Chaturvedi


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: https://github.com/brcsomnath/commonsense-sarcasm.
Anthology ID:
2021.insights-1.2
Volume:
Proceedings of the Second Workshop on Insights from Negative Results in NLP
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
insights
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9–15
Language:
URL:
https://aclanthology.org/2021.insights-1.2
DOI:
10.18653/v1/2021.insights-1.2
Bibkey:
Cite (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.
Cite (Informal):
Does Commonsense help in detecting Sarcasm? (Basu Roy Chowdhury & Chaturvedi, insights 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.insights-1.2.pdf
Software:
 2021.insights-1.2.Software.zip
Video:
 https://aclanthology.org/2021.insights-1.2.mp4
Code
 brcsomnath/commonsense-sarcasm
Data
Reddit