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We present a novel end-to-end trainable neural network model for task-oriented dialog systems.
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M. Henderson, “Machine learning for dialog state tracking: A review,” in
2015
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L. Shang, Z. Lu, and H. Li, “Neural responding machine for short-text conversation,”
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T. Zhao and M. Eskenazi, “Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning,” in
2016
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T.-H. Wen, D. Vandyke, N. Mrkšić, M. Gašić, L. M. Rojas-Barahona, P.-H. Su, S. Ultes, and S. Young, “A network-based end-to-end trainable task-oriented dialogue system,”
2016
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J. Perez and F. Liu, “Gated end-to-end memory networks,”
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X. Li, Y.-N. Chen, L. Li, and J. Gao, “End-to-end task-completion neural dialogue systems,”
2017
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M. Eric and C. D. Manning, “A copy-augmented sequence-to-sequence architecture gives good performance on task-oriented dialogue,” in
2017
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M. Seo, S. Min, A. Farhadi, and H. Hajishirzi, “Query-reduction networks for question answering,” in
2017
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