Lab Papers (GRASP)

Document Type

Journal Article

Date of this Version



Suggested Citation:
J. Graça, K. Ganchev and B. Taskar. (2010). "Learning TractableWord AlignmentModels with Complex Constraints." Computational Linguistics. Vol. 36(3). p. 481-504.

© 2010 MIT Press


Word-level alignment of bilingual text is a critical resource for a growing variety of tasks. Probabilistic models for word alignment present a fundamental trade-off between richness of captured constraints and correlations versus efficiency and tractability of inference. In this article, we use the Posterior Regularization framework (Graça, Ganchev, and Taskar 2007) to incorporate complex constraints into probabilistic models during learning without changing the efficiency of the underlying model. We focus on the simple and tractable hidden Markov model, and present an efficient learning algorithm for incorporating approximate bijectivity and symmetry constraints. Models estimated with these constraints produce a significant boost in performance as measured by both precision and recall of manually annotated alignments for six language pairs. We also report experiments on two different tasks where word alignments are required: phrase-based machine translation and syntax transfer, and show promising improvements over standard methods.

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Date Posted: 01 February 2011

This document has been peer reviewed.