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Video-based dialog task is a challenging multimodal learning task that hasreceived increasing attention over the past few years with state-of-the-artobtaining new performance records. This progress is largely powered by theadaptation of the more powerful transformer-based language encoders. Despitethis progress, existing approaches do not effectively utilize visual featuresto help solve tasks. Recent studies show that state-of-the-art models arebiased toward textual information rather than visual cues. In order to betterleverage the available visual information, this study proposes a new frameworkthat combines 3D-CNN network and transformer-based networks into a singlevisual encoder to extract more robust semantic representations from videos. Thevisual encoder is jointly trained end-to-end with other input modalities suchas text and audio. Experiments on the AVSD task show significant improvementover baselines in both generative and retrieval tasks.

End-to-End Multimodal Representation Learning for Video Dialog