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...

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Markos Mylonakis, Khalil Sima'an
• Keywords: EMNLP 2008, Natural Language Processing, Phrase Pairs, Phrase Translation, Reasonable Objective Function