  Multimodalities provide promising performance than unimodality in most tasks.
However, learning the semantic of the representations from multimodalities
efficiently is extremely challenging. To tackle this, we propose the
Transformer based Cross-modal Translator (TCT) to learn unimodal sequence
representations by translating from other related multimodal sequences on a
supervised learning method. Combined TCT with Multimodal Transformer Network
(MTN), we evaluate MTN-TCT on the video-grounded dialogue which uses
multimodality. The proposed method reports new state-of-the-art performance on
video-grounded dialogue which indicates representations learned by TCT are more
semantics compared to directly use unimodality.
