Xiaopeng Yang
2019
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders
Zhangming Chan
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Juntao Li
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Xiaopeng Yang
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Xiuying Chen
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Wenpeng Hu
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Dongyan Zhao
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Rui Yan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Variational autoencoders (VAEs) and Wasserstein autoencoders (WAEs) have achieved noticeable progress in open-domain response generation. Through introducing latent variables in continuous space, these models are capable of capturing utterance-level semantics, e.g., topic, syntactic properties, and thus can generate informative and diversified responses. In this work, we improve the WAE for response generation. In addition to the utterance-level information, we also model user-level information in latent continue space. Specifically, we embed user-level and utterance-level information into two multimodal distributions, and combine these two multimodal distributions into a mixed distribution. This mixed distribution will be used as the prior distribution of WAE in our proposed model, named as PersonaWAE. Experimental results on a large-scale real-world dataset confirm the superiority of our model for generating informative and personalized responses, where both automatic and human evaluations outperform state-of-the-art models.
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Co-authors
- Zhangming Chan 1
- Juntao Li 1
- Xiuying Chen 1
- Wenpeng Hu 1
- Dongyan Zhao 1
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- Rui Yan 1