Convolutional Neural Network Architectures for Matching Natural Language Sentences
Convolutional Neural Network Architectures for Matching Natural Language Sentences
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发表时间：
2014-12
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通讯作者：
Baotian Hu;Zhengdong Lu;Hang Li;Qingcai Chen
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作者：
Baotian Hu;Zhengdong Lu;Hang Li;Qingcai Chen
语义匹配对许多自然语言任务至关重要[2，28]。一个成功的匹配算法需要对语言对象的内部结构以及它们之间的相互作用进行充分的建模。作为实现这一目标的一步，我们提出了卷积神经网络模型，通过在视觉和语音上采用卷积策略来匹配两个句子。该模型不仅能很好地表达句子的层次化结构，而且能够捕捉到不同层次上丰富的匹配模式。我们的模型相当通用，不需要语言方面的先验知识，因此可以应用于不同性质和不同语言的匹配任务。通过对多种匹配任务的实证研究，验证了该模型在各种匹配任务上的有效性及其相对于竞争者模型的优势。
Semantic matching is of central importance to many natural language tasks [2,28]. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.