Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

被引：0

作者：

Miceli-Barone, Antonio Valerio ^{[1
]}

Attardi, Giuseppe ^{[1
]}

机构：

[1] Univ Pisa, Largo B Pontecorvo 3, I-56127 Pisa, Italy

来源：

PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1 | 2015年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The quality of statistical machine translation performed with phrase based approaches can be increased by permuting the words in the source sentences in an order which resembles that of the target language. We propose a class of recurrent neural models which exploit source-side dependency syntax features to reorder the words into a target-like order. We evaluate these models on the German-to-English and Italian-to-English language pairs, showing significant improvements over a phrase-based Moses baseline. We also compare with state of the art German-to-English pre-reordering rules, showing that our method obtains similar or better results.

引用

页码：846 / 856

页数：11