The factored statistical machine translation model uses a log-linear approach, in order to combine the several components, including the language model, the reordering model, the translation models and the generation models.

Traditional statistical machine translation methods are based on mapping on the lexical level, which takes place in a local window of a few words.

Our method is based on factored phrase-based statistical machine translation models.

Traditional statistical machine translation models deal with this problems in two ways:

We show that combining them with word—based n—gram models in the log—linear model of a state—of—the—art statistical machine translation system leads to improvements in translation quality as indicated by the BLEU score.

In the subsequent experiments, we use a phrase-based statistical machine translation system based on the log-linear formulation of the problem described in (Och and Ney, 2002):

However, in the area of statistical machine translation , especially in the context of large training corpora, fewer experiments with class-based n-gram models have been performed with mixed success (Raab, 2006).

We then show that using partially class-based language models trained using the resulting classifications together with word-based language models in a state-of-the-art statistical machine translation system yields improvements despite the very large size of the word-based models used.

We present a method to transliterate names in the framework of end-to-end statistical machine translation .

We have shown that a state-of-the-art statistical machine translation system can benefit from a dedicated transliteration module to improve the transla-

State-of-the-art statistical machine translation (SMT) is bad at translating names that are not very common, particularly across languages with different character sets and sound systems.

In this paper, we propose a novel string-to-dependency algorithm for statistical machine translation .

In this paper, we propose a novel string-to-dependency algorithm for statistical machine translation .

In recent years, hierarchical methods have been successfully applied to Statistical Machine Translation (Graehl and Knight, 2004; Chiang, 2005; Ding and Palmer, 2005; Quirk et al., 2005).

Conventional statistical machine translation (SMT) systems do not perform well on measure word generation due to data sparseness and the potential long distance dependency between measure words and their corresponding head words.

Our model works as a postprocessing procedure over output of statistical machine translation systems, and can work with any SMT system.

In most statistical machine translation (SMT) models (Och et al., 2004; Koehn et al., 2003; Chiang, 2005), some of measure words can be generated without modification or additional processing.