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Baotian Hu
Researcher at Harbin Institute of Technology
Publications - 57
Citations - 2860
Baotian Hu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 12, co-authored 50 publications receiving 2365 citations. Previous affiliations of Baotian Hu include Tencent & Harbin Institute of Technology Shenzhen Graduate School.
Papers
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Proceedings Article
Convolutional Neural Network Architectures for Matching Natural Language Sentences
TL;DR: Convolutional neural network models for matching two sentences are proposed, by adapting the convolutional strategy in vision and speech and nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling.
Posted Content
Convolutional Neural Network Architectures for Matching Natural Language Sentences
TL;DR: This paper proposed convolutional neural network models for matching two sentences, which can be applied to matching tasks of different nature and in different languages and demonstrate the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.
Proceedings ArticleDOI
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
TL;DR: Wang et al. as mentioned in this paper introduced a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public and consists of over 2 million real Chinese short texts with short summaries given by the author of each text.
Proceedings Article
Learning to extract coherent summary via deep reinforcement learning
Yuxiang Wu,Baotian Hu +1 more
TL;DR: This paper proposed a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns, which obviates the need for feature engineering and can be trained in an end-to-end fashion using unlabeled data.
Journal ArticleDOI
Recurrent convolutional neural network for answer selection in community question answering
TL;DR: The results prove the effectiveness of the proposed model on the task of answer selection in CQA, which achieves the best performance of Macro-F1 58.77%, which is 1.6% higher than the best submitted system of the answer selection task in SemEval2015.