Effective phrase translation extraction from alignment models

被引：0

作者：

Venugopal, A ^{[1
]}

Vogel, S ^{[1
]}

Waibel, A ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA

来源：

41ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE | 2003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：

Phrase level translation models are effective in improving translation quality by addressing the problem of local re-ordering across language boundaries. Methods that attempt to fundamentally modify the traditional IBM translation model to incorporate phrases typically do so at a prohibitive computational cost. We present a technique that begins with improved IBM models to create phrase level knowledge sources that effectively represent local as well as global phrasal context. Our method is robust to noisy alignments at both the sentence and corpus level, delivering high quality phrase level translation pairs that contribute to significant improvements in translation quality (as measured by the BLEU metric) over word based lexica as well as a competing alignment based method.

引用

页码：319 / 326

页数：8