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

Yao-Hung Hubert Tsai, Martin Ma, Muqiao Yang, Ruslan Salakhutdinov, Louis-Philippe Morency
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)  
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose Multimodal Routing, which dynamically adjusts weights between input modalities and output representations differently for each
more » ... t sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality features. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, mean-while keeping competitive performance compared to state-of-the-art methods.
doi:10.18653/v1/2020.emnlp-main.143 pmid:33969363 pmcid:PMC8106385 fatcat:fknnqv6a6zbx7fub66j6mvilvy