More specifically, our paper makes the following contributions: (1) We curate a new dataset, MUStARD, for multimodal sarcasm research with high-quality annotations, including both mutlimodal and conversational context features; (2) We exemplify various scenarios where incongruity in sarcasm is evident across different modalities, thus stressing the role of multimodal approaches to solve this problem; (3) We introduce several baselines and show that multimodal models are significantly more effective when compared to their unimodal variants; and (4) We also provide preceding turns in the dialogue which act as context information.
we surmise that this property of MUStARD leads to a new sub-task for future work: sarcasm detection in conversational context.