Multimodal Dialogue State Tracking By QA Approach with Data Augmentation

By Xiangyang Mou, Brandyn Sigouin, I. Steenstra, Hui Su. 2020

11 citations recorded by the source corpus.

Recently, a more challenging state tracking task, Audio-Video Scene-Aware Dialogue (AVSD), is catching an increasing amount of attention among researchers. Different from purely text-based dialogue state tracking, the dialogue in AVSD contains a sequence of question-answer pairs about a video and the final answer to the given question requires additional understanding of the video. This paper interprets the AVSD task from an open-domain Question Answering (QA) point of view and proposes a multimodal open-domain QA system to deal with the problem. The proposed QA system uses common encoder-decoder framework with multimodal fusion and attention. Teacher forcing is applied to train a natural language generator. We also propose a new data augmentation approach specifically under QA assumption. Our experiments show that our model and techniques bring significant improvements over the baseline model on the DSTC7-AVSD dataset and demonstrate the potentials of our data augmentation techniques.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Question Answering & Reading Comprehension, Dialogue & Conversational AI

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In Artificial Intelligence

  1. Wanxiang Che
  2. Bohan Li
  3. Yutai Hou

In Computer Vision and Pattern Recognition

  1. Wanxiang Che
  2. Bohan Li
  3. Yutai Hou

Citations in the corpus