The currently best performing statistical machine translation systems are based on phrase-based models: models that translate small word sequences at a time. This chapter explains the basic principles of phrase-based models and how they are trained, and takes a more detailed look at extensions to the main components: the translation model and the reordering model. The next chapter will explain the algorithms that are used to translate sentences using these models.

Standard Model

First, we lay out the standard model for phrase-based statistical machine translation. While there are many variations, these can all be seen as extensions to this model.

Motivation for Phrase-Based Models

The previous chapter introduced models for machine translation that were based on the translation of words. But words may not be the best candidates for the smallest units for translation. Sometimes one word in a foreign language translates into two English words, or vice versa. Word-based models often break down in these cases.

Consider Figure 5.1, which illustrations how phrase-based models work. The German input sentence is first segmented into so-called phrases(any multiword units). Then, each phrase is translated into an English phrase. Finally, phrases may be reordered. In Figure 5.1, the six German words and eight English words are mapped as five phrase pairs.