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EMNLP
2008

Phrase Translation Probabilities with ITG Priors and Smoothing as Learning Objective

9 years 5 months ago
Phrase Translation Probabilities with ITG Priors and Smoothing as Learning Objective
The conditional phrase translation probabilities constitute the principal components of phrase-based machine translation systems. These probabilities are estimated using a heuristic method that does not seem to optimize any reasonable objective function of the word-aligned, parallel training corpus. Earlier efforts on devising a better understood estimator either do not scale to reasonably sized training data, or lead to deteriorating performance. In this paper we explore a new approach based on three ingredients (1) A generative model with a prior over latent segmentations derived from Inversion Transduction Grammar (ITG), (2) A phrase table containing all phrase pairs without length limit, and (3) Smoothing as learning objective using a novel Maximum-A-Posteriori version of Deleted Estimation working with Expectation-Maximization. Where others conclude that latent segmentations lead to overfitting and deteriorating performance, we show here that these three ingredients give performa...
Markos Mylonakis, Khalil Sima'an
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where EMNLP
Authors Markos Mylonakis, Khalil Sima'an
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