Hierarchical multimodal attention for end-to-end audio-visual scene-aware dialogue response generation

Hierarchical multimodal attention for end-to-end audio-visual scene-aware dialogue response generation
Title:
Hierarchical multimodal attention for end-to-end audio-visual scene-aware dialogue response generation
Other Titles:
Computer Speech & Language
Keywords:
Publication Date:
29 March 2020
Citation:
Hung Le, Doyen Sahoo, Nancy F. Chen, Steven C.H. Hoi, Hierarchical multimodal attention for end-to-end audio-visual scene-aware dialogue response generation, Computer Speech & Language, Volume 63, 2020, 101095, ISSN 0885-2308, https://doi.org/10.1016/j.csl.2020.101095.
Abstract:
Dialogue System Technology Challenge (DSTC7), where we participated in the Audio Visual Scene-aware Dialogue System (AVSD) track. The AVSD track evaluates how dialogue systems understand video scenes and responds to users about the video visual and audio content. We propose a hierarchical attention approach on user queries, video caption, audio and visual features that contribute to improved evaluation results. We also apply a nonlinear feature fusion approach to combine the visual and audio features for better knowledge representation. Our proposed model shows superior performance in terms of both objective evaluation and human rating as compared to the baselines. In this extended work, we also provide a more extensive review of the related work, conduct additional experiments with word-level and context-level pretrained embeddings, and investigate different qualitative aspects of the generated responses.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
The first author is supported by A*STAR Computing and Information Science scholarship (formerly A*STAR Graduate scholarship). The third author is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046).
Description:
ISSN:
0885-2308
1095-8363
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