Paper: Discriminative Word Alignment With Conditional Random Fields

ACL ID P06-1009
Title Discriminative Word Alignment With Conditional Random Fields
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006

In this paper we present a novel approach for inducing word alignments from sen- tence aligned data. We use a Condi- tional Random Field (CRF), a discrimina- tive model, which is estimated on a small supervised training set. The CRF is condi- tioned 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 demonstratethatevenwithonlyafewhun- dred word-aligned training sentences, our model improves over the current state-of- the-art wi...