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2015

Context-Dependent Translation Selection Using Convolutional Neural Network

3 years 9 months ago
Context-Dependent Translation Selection Using Convolutional Neural Network
We propose a novel method for translation selection in statistical machine translation, in which a convolutional neural network is employed to judge the similarity between a phrase pair in two languages. The specifically designed convolutional architecture encodes not only the semantic similarity of the translation pair, but also the context containing the phrase in the source language. Therefore, our approach is able to capture context-dependent semantic similarities of translation pairs. We adopt a curriculum learning strategy to train the model: we classify the training examples into easy, medium, and difficult categories, and gradually build the ability of representing phrases and sentencelevel contexts by using training examples from easy to difficult. Experimental results show that our approach significantly outperforms the baseline system by up to
Baotian Hu, Zhaopeng Tu, Zhengdong Lu, Hang Li, Qi
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Baotian Hu, Zhaopeng Tu, Zhengdong Lu, Hang Li, Qingcai Chen
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