RNN-based Derivation Structure Prediction for SMT

被引：0

作者：

Zhai, Feifei ^{[1
]}

Zhang, Jiajun ^{[1
]}

Zhou, Yu ^{[1
]}

Zong, Chengqing ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China

来源：

PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | 2014年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we propose a novel derivation structure prediction (DSP) model for SMT using recursive neural network (RNN). Within the model, two steps are involved: (1) phrase-pair vector representation, to learn vector representations for phrase pairs; (2) derivation structure prediction, to generate a bilingual RNN that aims to distinguish good derivation structures from bad ones. Final experimental results show that our DSP model can significantly improve the translation quality.

引用

页码：779 / 784

页数：6

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