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Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances.
Jointly optimizing diversity and relevance in neural response generation
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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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Ryan Lowe, Michael Noseworthy, Iulian Vlad Serban, Nicolas Angelard-Gontier, Yoshua Bengio, and Joelle Pineau. 2017 · 2017
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Training millions of personalized dialogue agents
Pierre-Emmanuel Mazare, Samuel Humeau, Martin Raison, and Antoine Bordes. 2018 · 2018
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Towards exploiting background knowledge for building conversation systems
Nikita Moghe, Siddhartha Arora, Suman Banerjee, and Mitesh M. Khapra. 2018 · 2018
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Multi-turn dialogue response generation in an adversarial learning framework
Oluwatobi Olabiyi, Alan Salimov, Anish Khazane, and Erik Mueller. 2018 · 2018
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Unsupervised learning of sentence embeddings using compositional n-gram features
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Fine grained knowledge transfer for personalized task-oriented dialogue systems
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Yuanlong Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, and Ray Kurzweil. 2017 · 2017
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Better conversations by modeling, filtering, and optimizing for coherence and diversity
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Generating informative and diverse conversational responses via adversarial information maximization
Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, and Bill Dolan. 2018c · 2018
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Unsupervised discrete sentence representation learning for interpretable neural dialog generation
Tiancheng Zhao, Kyusong Lee, and Maxine Eskenazi. 2018 · 2018
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Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018 · 2018
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Deep learning based chatbot models
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Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, and Sunghun Kim. 2019 · 2019
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