Due to the richness of Chinese abbreviations, many of them may not appear in available parallel corpora, in which case current machine translation systems simply treat them as unknown words and leave them untranslated.

Our method does not require any additional annotated data other than the data that a regular translation system uses.

We integrate our method into a state-of-the-art baseline translation system and show that it consistently improves the performance of the baseline system on various NIST MT test sets.

Our method is scalable enough to handle large amount of monolingual data, and is essentially unsupervised as it does not require any additional annotated data than the baseline translation system .

We integrate our method into a state-of-the-art phrase-based baseline translation system , i.e., Moses (Koehn et al., 2007), and show that the integrated system consistently improves the performance of the baseline system on various NIST machine translation test sets.

o Step-2: translate the list into Chinese using a baseline translation system ;

Step-4 and -5 are natural ways to integrate the abbreviation translation component with the baseline translation system .

However, since most of statistical translation models (Koehn et al., 2003; Chiang, 2007; Galley et al., 2006) are symmetrical, it is relatively easy to train a translation system to translate from English to Chinese, except that we need to train a Chinese language model from the Chinese monolingual data.

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.

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.

We use the distributed training and application infrastructure described in (Brants et al., 2007) with modifications to allow the training of predictive class-based models and their application in the decoder of the machine translation system .

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.

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):

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.

This second problem is very difficult to address with word-based translation systems , when the relevant morphological information in the target language is either nonexistent or implicitly encoded in the source language.

We integrated the inflection prediction model with two types of machine translation systems : systems that make use of syntax and surface phrase-based systems.

4.1 Treelet translation system

4.2 Phrasal translation system

Other work closely related to ours is (Toutanova and Suzuki, 2007), which uses an independently trained case marker prediction model in an English-Japanese translation system , but it focuses on the problem of generating a small set of closed class words rather

Automatic word alignment is a key step in training statistical machine translation systems .

We propose and extensively evaluate a simple method for using alignment models to produce alignments better-suited for phrase-based MT systems, and show significant gains (as measured by BLEU score) in end-to-end translation systems for six languages pairs used in recent MT competitions.

Unfortunately, as was shown by Fraser and Marcu (2007) AER can have weak correlation with translation performance as measured by BLEU score (Pa-pineni et al., 2002), when the alignments are used to train a phrase-based translation system .

In the next section we evaluate and compare the effects of the different alignments in a phrase based machine translation system .

We trained several phrasal translation systems , varying only the word alignment (or phrasal alignment) method.

Most state-of—the-art statistical machine translation systems are based on large phrase tables extracted from parallel text using word-level alignments.

While this approach has been very successful, poor word-level alignments are nonetheless a common source of error in machine translation systems .

From this alignment, phrase pairs are extracted in the usual manner, and a phrase-based translation system is trained.