Google Research

A Class-Based Agreement Model For Generating Accurately Inflected Translations

  • Spence Green
  • John DeNero
50th Annual Meeting of the Association for Computational Linguistics (ACL 2012)


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.

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