Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

By Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria. Annual Meeting of the Association for Computational Linguistics, 2019

400 citations recorded by the source corpus.

Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic classification of sarcasm. As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows. MUStARD consists of audiovisual utterances annotated with sarcasm labels. Each utterance is accompanied by its context of historical utterances in the dialogue, which provides additional information on the scenario where the utterance occurs. Our initial results show that the use of multimodal information can reduce the relative error rate of sarcasm detection by up to 12.9% in F-score when compared to the use of individual modalities. The full dataset is publicly available for use at https://github.com/soujanyaporia/MUStARD.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/6a040a5dcc223538b4c8dc775df891c97c9b4fbd

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In Artificial Intelligence

In Experimental and Cognitive Psychology

Citations in the corpus

A survey on sentiment analysis methods, applications, and challenges (2022, methodology): “Numerous methods have been proposed (Castro et al. 2019; Medhat et al. 2014) for detecting sarcasm in language.”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “[18] recently released a multimodal sarcasm detection dataset, MUStARD, that takes conversational context into account.”
Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions (2022, background): “Some of the works include the work of [81], in which a new dataset, MUStARD is created for multimodal sarcasm research with high-quality annotations, including both mutlimodal and conversational context features.”