  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
