Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation
Karthik Visweswariah, Jiří Navrátil, Jeffrey S. Sorensen, Vijil Chenthamarakshan, Nandakishore Kambhatla · 2010
Syntax based reordering has been shown to be an effective way of handling word order differences between source and target languages in Statistical Machine Translation (SMT) systems. We present a simple, automatic method to learn rules that reorder source sentences to more closely match the target language word order using only a source side parse tree and automatically generated alignments. The resulting rules are applied to source language inputs as a pre-processing step and demonstrate significant improvements in SMT systems across a variety of languages pairs including English to Hindi, EnglishtoSpanishandEnglishtoFrench as measured on a variety of internal test sets as well as a public test set. 1