  In the past decade, sarcasm detection has been intensively conducted in a
textual scenario. With the popularization of video communication, the analysis
in multi-modal scenarios has received much attention in recent years.
Therefore, multi-modal sarcasm detection, which aims at detecting sarcasm in
video conversations, becomes increasingly hot in both the natural language
processing community and the multi-modal analysis community. In this paper,
considering that sarcasm is often conveyed through incongruity between
modalities (e.g., text expressing a compliment while acoustic tone indicating a
grumble), we construct a Contras-tive-Attention-based Sarcasm Detection
(ConAttSD) model, which uses an inter-modality contrastive attention mechanism
to extract several contrastive features for an utterance. A contrastive feature
represents the incongruity of information between two modalities. Our
experiments on MUStARD, a benchmark multi-modal sarcasm dataset, demonstrate
the effectiveness of the proposed ConAttSD model.
