Class-Based Agreement Model For Generating Accurately Inflected Translations
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.