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Advances in multimodal models have greatly improved how interactions relevantto various tasks are modeled. Today's multimodal models mainly focus on thecorrespondence between images and text, using this for tasks like image-textmatching. However, this covers only a subset of real-world interactions. Novelinteractions, such as sarcasm expressed through opposing spoken words andgestures or humor expressed through utterances and tone of voice, remainchallenging. In this paper, we introduce an approach to enhance multimodalmodels, which we call Multimodal Mixtures of Experts (MMoE). The key idea inMMoE is to train separate expert models for each type of multimodalinteraction, such as redundancy present in both modalities, uniqueness in onemodality, or synergy that emerges when both modalities are fused. On a sarcasmdetection task (MUStARD) and a humor detection task (URFUNNY), we obtain newstate-of-the-art results. MMoE is also able to be applied to various types ofmodels to gain improvement.

MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts