Training a Parser for Machine Translation Reordering
Venue
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP '11)
Publication Year
2011
Authors
Jason Katz-Brown, Slav Petrov, Ryan McDonald, Franz Och, David Talbot, Hiroshi Ichikawa, Masakazu Seno
BibTeX
Abstract
We propose a simple training regime that can improve the extrinsic performance of a
parser, given only a corpus of sentences and a way to automatically evaluate the
extrinsic quality of a candidate parse. We apply our method to train parsers that
excel when used as part of a reordering component in a statistical machine
translation system. We use a corpus of weakly-labeled reference reorderings to
guide parser training. Our best parsers contribute significant improvements in
subjective translation quality while their intrinsic attachment scores typically
regress.
