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Human communication is multimodal in nature; it is through multiplemodalities such as language, voice, and facial expressions, that opinions andemotions are expressed. Data in this domain exhibits complex multi-relationaland temporal interactions. Learning from this data is a fundamentallychallenging research problem. In this paper, we propose Modal-TemporalAttention Graph (MTAG). MTAG is an interpretable graph-based neural model thatprovides a suitable framework for analyzing multimodal sequential data. Wefirst introduce a procedure to convert unaligned multimodal sequence data intoa graph with heterogeneous nodes and edges that captures the rich interactionsacross modalities and through time. Then, a novel graph fusion operation,called MTAG fusion, along with a dynamic pruning and read-out technique, isdesigned to efficiently process this modal-temporal graph and capture variousinteractions. By learning to focus only on the important interactions withinthe graph, MTAG achieves state-of-the-art performance on multimodal sentimentanalysis and emotion recognition benchmarks, while utilizing significantlyfewer model parameters.

MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences