Paper: A Discriminative Matching Approach To Word Alignment

ACL ID H05-1010
Title A Discriminative Matching Approach To Word Alignment
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005

We present a discriminative, large- margin approach to feature-based matching for word alignment. In this framework, pairs of word tokens re- ceive a matching score, which is based on features of that pair, including mea- sures of association between the words, distortion between their positions, sim- ilarity of the orthographic form, and so on. Even with only 100 labeled train- ing examples and simple features which incorporate counts from a large unla- beled corpus, we achieve AER perfor- mance close to IBM Model 4, in much less time. Including Model 4 predic- tions as features, we achieve a relative AER reduction of 22% in over inter- sected Model 4 alignments.