Phrase-Based Alignment Models for Statistical Machine Translation

The first pattern recognition approaches to machine translation were based on single-word models. However, these models present an important deficiency; they do not take contextual information into account for the translation decision. The phrase-based approach consists in translating a multiword source sequence into a multiword target sequence, instead of a single source word into a single target word. We present different methods to train the parameters of this kind of model. In the evaluation phase of this approach, we obtained interesting results in comparison with other statistical models.