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    <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.</abstract>
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%0 Conference Proceedings
%T Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems
%A Le, Hung
%A Sahoo, Doyen
%A Chen, Nancy
%A Hoi, Steven
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F le-etal-2019-multimodal
%X 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.
%R 10.18653/v1/P19-1564
%U https://aclanthology.org/P19-1564
%U https://doi.org/10.18653/v1/P19-1564
%P 5612-5623

Markdown (Informal)

[Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems](https://aclanthology.org/P19-1564) (Le et al., ACL 2019)

• Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (Le et al., ACL 2019)

ACL

• Hung Le, Doyen Sahoo, Nancy Chen, and Steven Hoi. 2019. Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5612–5623, Florence, Italy. Association for Computational Linguistics.