Steven Hoi
2019
Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems
Hung Le
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Doyen Sahoo
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Nancy Chen
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Steven Hoi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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.
2015
SOLAR: Scalable Online Learning Algorithms for Ranking
Jialei Wang
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Ji Wan
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Yongdong Zhang
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Steven Hoi
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
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Co-authors
- Hung Le 1
- Doyen Sahoo 1
- Nancy Chen 1
- Jialei Wang 1
- Ji Wan 1
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Venues
- ACL2