Discriminative Word Alignment with Conditional Random Fields

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Discriminative Word Alignment with Conditional Random Fields
In this paper we present a novel approach for inducing word alignments from sentence aligned data. We use a Conditional Random Field (CRF), a discriminative model, which is estimated on a small supervised training set. The CRF is conditioned on both the source and target texts, and thus allows for the use of arbitrary and overlapping features over these data. Moreover, the CRF has efficient training and decoding processes which both find globally optimal solutions. We apply this alignment model to both French-English and Romanian-English language pairs. We show how a large number of highly predictive features can be easily incorporated into the CRF, and demonstrate that even with only a few hundred word-aligned training sentences, our model improves over the current state-ofthe-art with alignment error rates of 5.29 and 25.8 for the two tasks respectively.
Phil Blunsom, Trevor Cohn
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Phil Blunsom, Trevor Cohn
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