S
Shuzi Niu
Researcher at Chinese Academy of Sciences
Publications - 11
Citations - 697
Shuzi Niu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Latent variable & Dialog box. The author has an hindex of 6, co-authored 7 publications receiving 657 citations.
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Proceedings Article
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
TL;DR: This paper developed a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects, such as human-written and less noisy language, the dialogues in the dataset reflect our daily communication way and cover various topics about our daily life.
Proceedings ArticleDOI
A Conditional Variational Framework for Dialog Generation
TL;DR: The authors proposed a framework allowing conditional response generation based on specific attributes, such as genericness and sentiment states, which can be either manually assigned or automatically detected to generate meaningful responses in accordance with the specified attributes.
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DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
TL;DR: This article developed a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects, such as human-written and less noisy language, the dialogues in the dataset reflect our daily communication way and cover various topics about our daily life.
Proceedings Article
Improving Variational Encoder-Decoders in Dialogue Generation
TL;DR: The authors separate the training step into two phases: the first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learn to generalize latent representations by reconstructing the encoded embedding.
Posted Content
Improving Variational Encoder-Decoders in Dialogue Generation.
TL;DR: The authors separate the training step into two phases: the first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learn to generalize latent representations by reconstructing the encoded embedding.