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	Context-Aware Tree-Based Convolutional Neural Networks for Natural Language Inference
	Meng, Zhao ; Mou, Lili ; Li, Ge ; Jin, Zhi
	2016
关键词	Context-awareness Tree-based convolutional neural network Natural language inference
英文摘要	Natural language inference (NLI) aims to judge the relation between a premise sentence and a hypothesis sentence. In this paper, we propose a context-aware tree-based convolutional neural network (TBCNN) to improve the performance of NLI. In our method, we utilize tree-based convolutional neural networks, which are proposed in our previous work, to capture the premise's and hypothesis's information. In this paper, to enhance our previous model, we summarize the premise's information in terms of both word level and convolution level by dynamic pooling and feed such information to the convolutional layer when we model the hypothesis. In this way, the tree-based convolutional sentence model is context-aware. Then we match the sentence vectors by heuristics including vector concatenation, element-wise difference/product so as to remain low computational complexity. Experiments show that the performance of our context-aware variant achieves better performance than individual TBCNNs.; CPCI-S(ISTP); zhaomeng.pku@outlook.com; doublepower.mou@gmail.com; lige@sei.pku.edu.cn; zhijin@sei.pku.edu.cn; 515-526; 9983
语种	英语
出处	9th International Conference on Knowledge Science, Engineering, and Management (KSEM)
DOI标识	10.1007/978-3-319-47650-6_41
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/459978]
专题	信息科学技术学院 软件与微电子学院
推荐引用方式 GB/T 7714	Meng, Zhao,Mou, Lili,Li, Ge,et al. Context-Aware Tree-Based Convolutional Neural Networks for Natural Language Inference. 2016-01-01.

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