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Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses.
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A diversity-promoting objective function for neural conversation models
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Structuring latent spaces for stylized response generation
Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2019b
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A repository of conversational datasets
Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšić, Georgios Spithourakis, Pei-Hao Su, Ivan Vulic, and Tsung-Hsien Wen. 2019a
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Personalizing dialogue agents: I have a dog, do you have pets too?
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End-to-end conversation modeling: Dstc7 task 2 description
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Jointly optimizing diversity and relevance in neural response generation
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Language models are unsupervised multitask learners
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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2020
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