Discriminative Modeling of Extraction Sets for Machine Translation

We present a discriminative model that directly predicts which set of phrasal translation rules should be extracted from a sentence pair. Our model scores extraction sets: nested collections of all the overlapping phrase pairs consistent with an underlying word alignment. Extraction set models provide two principle advantages over word-factored alignment models. First, we can incorporate features on phrase pairs, in addition to word links. Second, we can optimize for an extraction-based loss function that relates directly to the end task of generating translations. Our model gives improvements in alignment quality relative to state-of-the-art unsupervised and supervised baselines, as well

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: John DeNero, Dan Klein
• Keywords: ACL 2010, Computational Linguistics, Extraction-based Loss Function, Model Scores Extraction, Phrasal Translation Rules