Does Commonsense help in detecting Sarcasm?
This work addresses sarcasm detection for NLP applications like sentiment analysis, but it is incremental as it shows no improvement over existing methods.
The paper investigated whether incorporating commonsense knowledge improves sarcasm detection in NLP tasks, but found that the approach did not outperform the baseline model across three datasets.
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.