Discriminative Modeling of Extraction Sets for Machine Translation
John DeNero, Dan Klein · 2010
We present a discriminative model that di-rectly predicts which set of phrasal transla-tion rules should be extracted from a sen-tence pair. Our model scores extraction sets: nested collections of all the overlap-ping phrase pairs consistent with an under-lying word alignment. Extraction set mod-els 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 unsuper-vised and supervised baselines, as well as providing up to a 1.4 improvement in BLEU score in Chinese-to-English trans-lation experiments. 1