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We focus on multi-turn response selection in a retrieval-based dialog system.
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X. Zhou, D. Dong, H. Wu, S. Zhao, D. Yu, H. Tian, X. Liu, and R. Yan, “Multi-view response selection for human-computer conversation,” in
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard
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Y. Wu, W. Wu, C. Xing, M. Zhou, and Z. Li, “Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots,” in
2017
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Z. Zhang, J. Li, P. Zhu, H. Zhao, and G. Liu, “Modeling multi-turn conversation with deep utterance aggregation,” in
2018
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X. Zhou, L. Li, D. Dong, Y. Liu, Y. Chen, W. X. Zhao, D. Yu, and H. Wu, “Multi-turn response selection for chatbots with deep attention matching network,” in
2018
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M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in
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D. Chaudhuri, A. Kristiadi, J. Lehmann, and A. Fischer, “Improving response selection in multi-turn dialogue systems by incorporating domain knowledge,” in
2018
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2018
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in
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Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in
2019
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A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman, “Superglue: A stickier benchmark for general-purpose language understanding systems,” in
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C. Gunasekara, J. K. Kummerfeld, L. Polymenakos, and W. Lasecki, “DSTC7 task 1: Noetic end-to-end response selection,” in
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C. Tao, W. Wu, C. Xu, W. Hu, D. Zhao, and R. Yan, “One time of interaction may not be enough: Go deep with an interaction-over-interaction network for response selection in dialogues,” in
2019
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C. Yuan, W. Zhou, M. Li, S. Lv, F. Zhu, J. Han, and S. Hu, “Multi-hop selector network for multi-turn response selection in retrieval-based chatbots,” in
2019
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J. Vig and K. Ramea, “Comparison of transfer-learning approaches for response selection in multi-turn conversations,” in
2019
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2019
Closest in time.
C. Tao, W. Wu, C. Xu, W. Hu, D. Zhao, and R. Yan, “Multi-representation fusion network for multi-turn response selection in retrieval-based chatbots,” in
2019
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Q. Chen and W. Wang, “Sequential attention-based network for noetic end-to-end response selection,” in
2019
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N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in
2019
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