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We study multi-turn response generation for open-domain dialogues.
Unified language model pre-training for natural language understanding and generation
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Do neural dialog systems use the conversation history effectively? an empirical study
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Sequence to sequence learning with neural networks
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015 · 2015
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The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems
Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015 · 2015
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A neural attention model for abstractive sentence summarization
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Neural responding machine for short-text conversation
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A persona-based neural conversation model
Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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A hierarchical latent variable encoder-decoder model for generating dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C Courville, and Yoshua Bengio. 2017 · 2017
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Attention is all you need
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Topic aware neural response generation
Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017 · 2017
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018 · 2018
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Learning longer-term dependencies in rnns with auxiliary losses
Trieu Trinh, Andrew Dai, Thang Luong, and Quoc Le. 2018 · 2018
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Auxiliary objectives for neural error detection models
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Hierarchical recurrent attention network for response generation
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Personalizing dialogue agents: I have a dog, do you have pets too?
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Self-supervised dialogue learning
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Recosa: Detecting the relevant contexts with self-attention for multi-turn dialogue generation
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