Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems
Hung Le, Doyen Sahoo, Nancy Chen, Steven Hoi · 2019
Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and(2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding.Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos).To overcome this, we propose Multimodal Transformer Networks (MTN) to encode videos and incorporate information from different modalities.We also propose queryaware attention through an auto-encoder to extract query-aware features from non-text modalities.We develop a training procedure to simulate token-level decoding to improve the quality of generated responses during inference.We get state of the art performance on Dialogue System Technology Challenge 7 (DSTC7).Our model also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance.