Difference between revisions of "DeNero et al, EMNLP 2008"
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Unlike word-to-phrase alignments, computing the alignment expectations for phrase-to-phrase alignments is generally intractable due to the exponential growth of possible combination of phrases and alignments. Because of this, previous attempts for building a joint phrase alignment model have been unsuccessful. | Unlike word-to-phrase alignments, computing the alignment expectations for phrase-to-phrase alignments is generally intractable due to the exponential growth of possible combination of phrases and alignments. Because of this, previous attempts for building a joint phrase alignment model have been unsuccessful. | ||
| − | This paper describes the first tractable | + | This paper describes the first tractable phrase-to-phrase Alignment Model, which relies on Gibbs Sampling to tackle the intractability problem. |
Tests show translation improvements over Machine Translation Systems build using conventional methods. | Tests show translation improvements over Machine Translation Systems build using conventional methods. | ||
== Algorithm == | == Algorithm == | ||
Revision as of 13:38, 25 September 2011
Contents
Citation
Denero, J., Bouchard-ct, R., & Klein, D.(2008). Sampling alignment structure under a Bayesian translation model. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08). Association for Computational Linguistics, Stroudsburg, PA, USA, 314-323.
Online version
Summary
Unlike word-to-phrase alignments, computing the alignment expectations for phrase-to-phrase alignments is generally intractable due to the exponential growth of possible combination of phrases and alignments. Because of this, previous attempts for building a joint phrase alignment model have been unsuccessful.
This paper describes the first tractable phrase-to-phrase Alignment Model, which relies on Gibbs Sampling to tackle the intractability problem.
Tests show translation improvements over Machine Translation Systems build using conventional methods.
