Source-Side Classifier Preordering for Machine Translation
Venue
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP '13) (2013)
Publication Year
2013
Authors
BibTeX
Abstract
We present a simple and novel classifier-based preordering approach. Unlike
existing preordering models, we train feature-rich discriminative classifiers that
directly predict the target-side word order. Our approach combines the strengths of
lexical reordering and syntactic preordering models by performing long-distance
reorderings using the structure of the parse tree, while utilizing a discriminative
model with a rich set of features, including lexical features. We present extensive
experiments on 22 language pairs, including preordering into English from 7 other
languages. We obtain improvements of up to 1.4 BLEU on language pairs in the WMT
2010 shared task. For languages from different families the improvements often
exceed 2 BLEU. Many of these gains are also significant in human evaluations.
