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The human language can be expressed through multiple sources of informationknown as modalities, including tones of voice, facial gestures, and spokenlanguage. Recent multimodal learning with strong performances on human-centrictasks such as sentiment analysis and emotion recognition are often black-box,with very limited interpretability. In this paper we propose MultimodalRouting, which dynamically adjusts weights between input modalities and outputrepresentations differently for each input sample. Multimodal routing canidentify relative importance of both individual modalities and cross-modalityfeatures. Moreover, the weight assignment by routing allows us to interpretmodality-prediction relationships not only globally (i.e. general trends overthe whole dataset), but also locally for each single input sample, meanwhilekeeping competitive performance compared to state-of-the-art methods.

Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis