Does Commonsense help in detecting Sarcasm?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 17 Sep 2021.
Builds on
16 of 34 references.
References · 16
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4 more
- 2019Deep Short Text Classification with Knowledge Powered Attentioncited by 22
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- 2017Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialoguecited by 14
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Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.