A Class-Based Agreement Model For Generating Accurately Inflected Translations
Venue
50th Annual Meeting of the Association for Computational Linguistics (ACL 2012)
Publication Year
2012
Authors
Spence Green, John DeNero
BibTeX
Abstract
When automatically translating from a weakly inflected source language like English
to a target language with richer grammatical features such as gender and dual
number, the output commonly contains morpho-syntactic agreement errors. To address
this issue, we present a target-side, class-based agreement model. Agreement is
promoted by scoring a sequence of fine-grained morpho-syntactic classes that are
predicted during decoding for each translation hypothesis. For English-to-Arabic
translation, our model yields a +1.04 BLEU average improvement over a
state-of-the-art baseline. The model does not require bitext or phrase table
annotations and can be easily implemented as a feature in many phrase-based
decoders.
