Bridging Text and Video: A Universal Multimodal Transformer for Audio-Visual Scene-Aware Dialog
Zekang Li, Zongjia Li, Jinchao Zhang, Yang Feng, Jie Zhou
Abstract
Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challenge (DSTC8). There are two challenges in this task: 1) making effective interaction among different modalities; 2) better understanding dialogues and generating informative responses. To tackle the challenges, we propose a universal multimodal transformer and introduce the multi-task learning method to learn joint representations among different modalities as well as generate informative and fluent responses by leveraging the pre-trained language model. Our method extends the natural language generation pre-trained model to multimodal dialogue generation task, which allows fine-tuning language models to capture information across both visual and textual modalities. Our system achieves the best performance in the objective evaluation in both DSTC7-AVSD and DSTC8-AVSD dataset and achieves an impressive 98.4% of the human performance based on human ratings in the DSTC8-AVSD challenge.