Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

ACL 2017 Tiancheng ZhaoRan ZhaoMaxine Eskenazi

While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder... (read more)

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