Syntactic phrase-based statistical machine translation

被引：3

作者：

Hassan, Hany ^{[1
]}

Heame, Mary ^{[1
]}

Way, Andy ^{[1
]}

Sima'an, Khalil ^{[2
]}

机构：

[1] Dublin City Univ, Sch Comp, Dublin 9, Ireland

[2] Univ Amsterdam, ILLC, NL-1018 TV Amsterdam, Netherlands

来源：

2006 IEEE SPOKEN LANGUAGE TECHNOLOGY WORKSHOP | 2006年

基金：

爱尔兰科学基金会;

关键词：

D O I：

10.1109/SLT.2006.326799

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Phrase-based Statistical Machine Translation (PBSMT) systems represent the dominant approach in MT today. However, unlike systems in other paradigms, it has proven difficult to date to incorporate syntactic knowledge in order to improve translation quality. This paper improves on recent research which uses 'syntactified' target language phrases, by incorporating supertags as constraints to better resolve parse tree fragments. In addition, we do not impose any sentence-length limit, and using a log-linear decoder, we outperform a state-of-the-art PBSMT system by over 1.3 BLEU points (or 15 1 % relative) on the NIST 2003 Arabic-English test corpus.

引用

页码：238 / +

页数：3

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