Difference between revisions of "DeNero et al, EMNLP 2008"
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Tests show translation improvements over Machine Translation Systems build using conventional methods. | Tests show translation improvements over Machine Translation Systems build using conventional methods. | ||
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| + | == Previous Work == | ||
| + | Most Word Alignment Models can not model many-to-many alignments, since the models restrict each target word to be aligned with at most one source word. | ||
== Algorithm == | == Algorithm == | ||
Revision as of 13:40, 25 September 2011
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.
Previous Work
Most Word Alignment Models can not model many-to-many alignments, since the models restrict each target word to be aligned with at most one source word.
