Paper: A Word Reordering Model for Improved Machine Translation

ACL ID D11-1045
Title A Word Reordering Model for Improved Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

Preordering of source side sentences has proved to be useful in improving statistical machine translation. Most work has used a parser in the source language along with rules to map the source language word order into the target language word order. The require- ment to have a source language parser is a ma- jor drawback, which we seek to overcome in this paper. Instead of using a parser and then using rules to order the source side sentence we learn a model that can directly reorder source side sentences to match target word or- der using a small parallel corpus with high- quality word alignments. Our model learns pairwise costs of a word immediately preced- ing another word. We use the Lin-Kernighan heuristic to find the best source reordering ef- ficiently during training and testing an...