Towards String-to-Tree Neural Machine Translation

被引：56

作者：

Aharoni, Roee ^{[1
]}

Goldberg, Yoav ^{[1
]}

机构：

[1] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel

来源：

PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2 | 2017年

关键词：

D O I：

10.18653/v1/P17-2021

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. Experiments on the WMT16 German-English news translation task shown improved BLEU scores when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.

引用

页码：132 / 140

页数：9