Learning Tractable Word Alignment Models with Complex Constraints

@article{Graa2010LearningTW,
  title={Learning Tractable Word Alignment Models with Complex Constraints},
  author={Jo{\~a}o Graça and Kuzman Ganchev and Ben Taskar},
  journal={Computational Linguistics},
  year={2010},
  volume={36},
  pages={481-504}
}
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… CONTINUE READING
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