- Phrase translation probabilities with ITG priors and smoothing as learning objective
- 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), Honolulu, HI, USA
- Book/source title
- EMNLP 2008: 2008 Conference on Empirical Methods in Natural Language Processing: Proceedings of the conference
- Pages (from-to)
- Association for Computational Linguistics (ACL)
- Document type
- Conference contribution
- Interfacultary Research Institutes
- Institute for Logic, Language and Computation (ILLC)
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 performance equivalent to the heuristic method on reasonably sized training data.
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