Year: 2021
Type: conference-paper
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
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W3200043970