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Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people.
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An empirical study of cognition and theatrical improvisation
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Shared mental models in improvisational performance
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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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Not all dialogues are created equal: Instance weighting for neural conversational models
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The London-Lund corpus 2: A new corpus of spoken British English in the making
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A hierarchical latent variable encoder-decoder model for generating dialogues
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Generating long and diverse responses with neural conversation models
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Turn-taking with improvisational co-creative agents
Lauren Winston and Brian Magerko. 2017 · 2017
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Iulian Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, and Joelle Pineau. 2015 · 2015
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A neural network approach to context-sensitive generation of conversational responses
Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016a · 2016
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016c · 2016
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OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Pierre Lison and Jörg Tiedemann. 2016 · 2016
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Improvisational computational storytelling in open worlds
Lara J. Martin, Brent Harrison, and Mark O. Riedl. 2016 · 2016
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How podcasting is changing the audio storytelling genre
Siobhan McHugh. 2016 · 2016
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Neural response generation via GAN with an approximate embedding layer
Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang, Zhuoran Wang, and Chao Qi. 2017 · 2017
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Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017 · 2017
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Towards less generic responses in neural conversation models: A statistical re-weighting method
Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, and Shuming Shi. 2018 · 2018
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Personalizing dialogue agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Boosting dialog response generation
Wenchao Du and Alan W Black. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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Challenges in building intelligent open-domain dialog systems
Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020 · 2020
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