The experiments presented show that predictive class-based models trained using the obtained word classifications can improve the quality of a state-of-the-art machine translation system as indicated by the BLEU score in both translation tasks .

Instead we report BLEU scores (Papineni et al., 2002) of the machine translation system using different combinations of word- and class-based models for translation tasks from English to Arabic and Arabic to English.

Table 1 shows the BLEU scores reached by the translation system when combining the different class-based models with the word-based model in comparison to the BLEU scores by a system using only the word-based model on the Arabic-English translation task .

For our experiment with the English Arabic translation task we trained two 5-gram predictive class-based models with 512 clusters on the Arabic a'rng'gawO’r’d and a'rL’webne’ws data sets.

Experimental results on the NIST MT-2005 Chinese-English translation task show that our method statistically significantly outperforms the baseline systems.

The experimental results on the NIST MT-2005 Chinese-English translation task demonstrate the effectiveness of the proposed model.

Experiment results on the NIST MT-2005 Chinese-English translation task show that our method significantly outperforms Moses (Koehn et al., 2007), a state-of-the-art phrase-based SMT system, and other linguistically syntax-based methods, such as SCFG-based and STSG-based methods (Zhang et al., 2007).