We evaluate our procedure on translation forests from two large-scale, state-of-the-art hierarchical machine translation systems .

Forests arise naturally in chart-based decoding procedures for many hierarchical translation systems (Chiang, 2007).

generated already by the decoder of a syntactic translation system .

Modern statistical machine translation systems take as input some f and score each derivation 6 according to a linear model of features: A, -6i(f, e).

The distribution P(e| f) can be induced from a translation system’s features and weights by expo-nentiating with base I) to form a log-linear model:

We evaluate these consensus decoding techniques on two different full-scale state-of-the-art hierarchical machine translation systems .

SBMT is a string-to-tree translation system with rich target-side syntactic information encoded in the translation model.

Translation forests compactly encode distributions over much larger sets of derivations and arise naturally in chart-based decoding for a wide variety of hierarchical translation systems (Chiang, 2007; Galley et al., 2006; Mi et al., 2008; Venugopal et al., 2007).

In all, we show improvements of up to 1.0 BLEU from consensus approaches for state-of-the-art large-scale hierarchical translation systems .

We also extracted the Chinese-Spanish (CS) corpus to build a standard CS translation system , which is denoted as Standard.

To select translation among outputs produced by different pivot translation systems , we used SVM-light (Joachins, 1999) to perform support vector regression with the linear kernel.

For translations from one of the systems, this method uses the outputs from other translation systems as pseudo references.

The advantage of our method is that we do not need to manually label the translations produced by each translation system , therefore enabling our method suitable for translation selection among any systems without additional manual work.

Given a source sentence 3, we can translate it into n pivot sentences 191,192, ..., pn using a source-pivot translation system .

We propose a method to select the optimal translation from those produced by various translation systems .

For each translation, this method uses the outputs from other translation systems as pseudo references.

can easily retrain the learner under different conditions, therefore enabling our method to be applied to sentence-level translation selection from any sets of translation systems without any additional human work.

The experimental results show that the proposed method outperforms the baseline statistical machine translation system by 30.42%.

In order to evaluate the influence of segmentation results upon the statistical ON translation system , we compare the results of two translation models.

Then the phrase-based machine translation system MOSES2 is adopted to translate the 503 Chinese NEs in testing set into English.

Compared with the statistical ON translation model, we can see that the performance is improved from 18.29% to 48.71% (the bold data shown in column 1 and column 3 of Table 5) by using our Chinese-English ON translation system .

For solving these two problems, we propose a Chinese-English organization name translation system using heuristic web mining and asymmetric alignment, which has three innovations.

The Framework of our ON translation system shown in Figure 1 has four modules.

Recent research has shown substantial improvements can be achieved by utilizing consensus statistics obtained from outputs of multiple machine translation systems .

Typically, the resulting systems take outputs of individual machine translation systems as

A common property of all the work mentioned above is that the combination models work on the basis of n-best translation lists (full hypotheses) of existing machine translation systems .

The translation system obviates some information which, in context, is not considered crucial by the human assessors.

We consider the set of translations system presented in each competition as the translation approaches pool.

In addition, our Combined System Test shows that, when training a combined translation system , using metrics at several linguistic processing levels improves substantially the use of individual metrics.