A Simple and Effective Approach to Coverage-Aware Neural Machine Translation

被引：0

作者：

Li, Yanyang ^{[1
]}

Xiao, Tong ^{[1
]}

Li, Yinqiao ^{[1
]}

Wang, Qiang ^{[1
]}

Xu, Changming ^{[1
]}

Lu, Xueqiang ^{[2
]}

机构：

[1] Northeastern Univ, Nat Language Proc Lab, Boston, MA 02115 USA

[2] Beijing Key Lab Internet Culture & Digital Dissem, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | 2018年

基金：

美国国家科学基金会;

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：

We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT). Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs. Moreover, it is robust to large beam sizes, which is not well studied in previous work. On the Chinese-English and English-German translation tasks, our approach yields +0.4 similar to similar to 1.5 BLEU improvements over the state-of-the-art baselines.

引用

页码：292 / 297

页数：6

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