Discriminative word alignment with conditional random fields

Discriminative word alignment with conditional random fields (2006)

by Phil Blunsom , Trevor Cohn

Venue:	In Proc. of ACL-2006
Citations:	19 - 0 self

Datum	Value	Source
TITLE	Discriminative word alignment with conditional random fields	INFERENCE
AUTHOR NAME	Phil Blunsom	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Software Engineering and Computer Science; University of Melbourne	SVM HeaderParse 0.2
AUTHOR NAME	Trevor Cohn	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Software Engineering and Computer Science; University of Melbourne	SVM HeaderParse 0.2
ABSTRACT	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. 1	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In Proc. of ACL-2006	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	65--72	INFERENCE
CITATIONS	17 found	ParsCit 1.0