Multimodal learning is an emerging yet challenging research area. In this paper, we deal with multimodal sarcasm and humor detection from conversational videos and image-text pairs. Being a fleeting action, which is reflected across the modalities, sarcasm detection is challenging since large datasets are not available for this task in the literature. Therefore, we primarily focus on resource-constrained training, where the number of training samples is limited. To this end, we propose a novel multimodal learning system, MuLOT (Multimodal Learning using Optimal Transport), which utilizes self-attention to exploit intra-modal correspondence and optimal transport for cross-modal correspondence. Finally, the modalities are combined with multimodal attention fusion to capture the inter-dependencies across modalities. We test our approach for multimodal sarcasm and humor detection on three benchmark datasets - MUStARD (video, audio, text), UR-FUNNY (video, audio, text), MST (image, text) and obtain 2.1%, 1.54%, and 2.34% accuracy improvements over state-of-the-art.
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