Furthermore, integrated Model-III achieves overall 3.48 BLEU points improvement and 2.62 TER points reduction in comparison with the pure SMT system .

The experiments show that the proposed Model-III outperforms both the TM and the SMT systems significantly (p < 0.05) in either BLEU or TER when fuzzy match score is above 0.4.

Compared with the pure SMT system , Model-III achieves overall 3.48 BLEU points improvement and 2.62 TER points reduction on a Chinese—English TM database.

For the phrase-based SMT system , we adopted the Moses toolkit (Koehn et al., 2007).

We first extract 95% of the bilingual sentences as a new training corpus to train a SMT system .

Scores marked by “*” are significantly better ([9 < 0.05) than both the TM and the SMT systems .

Especially, there is no guarantee that a SMT system can produce translations in a consistent manner (Ma et al., 2011).

Afterwards, they merge the relevant translations of matched segments into the source sentence, and then force the SMT system to only translate those unmatched segments at decoding.

Compared with the pure SMT system , the proposed integrated Model-III achieves 3.48 BLEU points improvement and 2.62 TER points reduction overall.

We describe four versions of the model and implement an algorithm to integrate our proposed model into a syntax-based SMT system .

Integrating the TNO Model into syntax-based SMT systems is nontrivial, especially with the MOS modeling.

To show the effectiveness of our model, we integrate our TNO model into a state-of-the-art syntax-based SMT system , which uses synchronous context-free grammar (SCFG) rules to jointly model reordering and lexical translation.

We show the efficacy of our proposal in a large-scale Chinese-to-English translation task where the introduction of our TNO model provides a significant gain over a state-of-the-art string-to-dependency SMT system (Shen et al., 2008) that we enhance with additional state-of-the-art features.

Even though the experimental results carried out in this paper employ SCFG-based SMT systems, we would like to point out that our models is applicable to other systems including phrase-based SMT systems .

Each of these factors will act as an additional feature in the log-linear framework of our SMT system .

Evaluations of J apanese-to-English translation on the NTCIR-9 data show that our induced Japanese POS tags for dependency trees improve the performance of a forest-to-string SMT system .

We evaluated our bilingual infinite tree model for POS induction using an in-house developed syntax-based forest-to-string SMT system .

Under the Moses phrase-based SMT system (Koehn et al., 2007) with the default settings, we achieved a 26.80% BLEU score.

If we could discriminate POS tags for two cases, we might improve the performance of a Japanese-to-English SMT system .

Experiments are carried out on the NTCIR-9 Japanese-to-English task using a binarized forest-to-string SMT system with dependency trees as its source side.

The contribution of this paper is to improve the prediction of case in our SMT system by implementing and combining two alternative routes to integrate subcategorization information from the syntax-semantic interface: (i) We regard the translation as a function of the source language input, and project the syntactic functions of the English nouns to their German translations in the

Table 2 illustrates the different steps of the inflection process: the markup (number and gender on nouns) in the stemmed output of the SMT system is part of the input to the respective feature prediction.

In contrast, the SMT system often produces more isomorphic translations, which is helpful for annotating source-side features on the target language.