Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

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 query-aware 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.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Florence, Italy, 2019 July 28 - August 2

First Page

5612

Last Page

5623

Publisher

ACL

City or Country

Arlington, VA

Copyright Owner and License

Publisher

Additional URL

https://www.aclweb.org/anthology/P19-1564/

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