Intermediate-Task Transfer Learning with BERT for Sarcasm Detection

Edoardo Savini and Cornelia Caragea
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Edoardo Savini: Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
Cornelia Caragea: Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA

Mathematics, 2022, vol. 10, issue 5, 1-14

Abstract: Sarcasm detection plays an important role in natural language processing as it can impact the performance of many applications, including sentiment analysis, opinion mining, and stance detection. Despite substantial progress on sarcasm detection, the research results are scattered across datasets and studies. In this paper, we survey the current state-of-the-art and present strong baselines for sarcasm detection based on BERT pre-trained language models. We further improve our BERT models by fine-tuning them on related intermediate tasks before fine-tuning them on our target task. Specifically, relying on the correlation between sarcasm and (implied negative) sentiment and emotions, we explore a transfer learning framework that uses sentiment classification and emotion detection as individual intermediate tasks to infuse knowledge into the target task of sarcasm detection. Experimental results on three datasets that have different characteristics show that the BERT-based models outperform many previous models.

Keywords: sarcasm detection; intermediate-task transfer learning; emotion-enriched sarcasm detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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