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Baoxun Wang
Researcher at Tencent
Publications - 58
Citations - 940
Baoxun Wang is an academic researcher from Tencent. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 13, co-authored 42 publications receiving 783 citations. Previous affiliations of Baoxun Wang include Peking University & University of Macau.
Papers
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Proceedings ArticleDOI
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory
TL;DR: Long ShortTerm Memory (LSTM) recurrent network for twitter sentiment prediction is introduced, with the help of gates and constant error carousels in the memory block structure, to handle interactions between words through a flexible compositional function.
Proceedings ArticleDOI
Neural Response Generation via GAN with an Approximate Embedding Layer.
TL;DR: The proposed GAN setup provides an effective way to avoid noninformative responses (a.k.a “safe responses”) in traditional neural response generators, and significantly outperforms existing neural response generation models in diversity metrics.
Posted Content
Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling.
TL;DR: The loose structured domain knowledge base is introduced, which can be built with slight amount of manual work and easily adopted by the Recall gate, so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations.
Proceedings Article
Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities
TL;DR: A deep belief network is proposed to model the semantic relevance for question-answer pairs and the experimental results show that the method outperforms the traditional approaches on both the cQA and the forum corpora.
Journal ArticleDOI
Content-Oriented User Modeling for Personalized Response Ranking in Chatbots
TL;DR: The approach is hopeful to represent users’ personal information implicitly based on user generated contents, and it is promising to perform as an important component in chatbots to select the personalized responses for each user.