Experimental results on spoken language translation show that this hybrid method significantly improves the translation quality , which outperforms the method using a source-target corpus of the same size.

Translation quality was evaluated using both the BLEU score proposed by Papineni et al.

In our experiments, only l-best Chinese or Spanish translation was used since using n-best results did not greatly improve the translation quality .

From the translation results, it can be seen that three methods achieved comparable translation quality on both ASR and CRR inputs, with the translation results on CRR inputs are much better than those on ASR inputs because of the errors in the ASR inputs.

As a result, we can build a synthetic multilingual corpus, which can be used to improve the translation quality .

The idea of using RBMT systems to improve the translation quality of SMT sysems has been explored in Hu et al.

Although previous studies proposed several pivot translation methods, there are no studies to combine different pivot methods for translation quality improvement.

The translated test set can be added to the training data to further improve translation quality .

Additionally, we remove low confidence alignment links from the word alignment of a bilingual training corpus, which increases the alignment F-score, improves Chinese-English and Arabic-English translation quality and significantly reduces the phrase translation table size.

In section 4 we show how to improve a MaXEnt word alignment quality by removing low confidence alignment links, which also leads to improved translation quality as shown in section 5.

This is similar to the ”loose phrases” described in (Ayan and Dorr, 2006a), which increased the number of correct phrase translations and improved the translation quality .

We measure the translation quality with automatic metrics including BLEU (Papineni et al., 2001) and TER (Snover et al., 2006).

The higher the BLEU score is, or the lower the TER score is, the better the translation quality is.

For newswire, the translation quality is improved by 0.44 on the whole test set and 1.1 on the tail documents, as measured by (TER-BLEU)/2.

The translation quality is measured by TQ for individual systems M Fd_, E; it can be BLEU score or WEM’ER (Word error rate and position independent WER) which induces a maximization or minimization problem, respectively.

This process is continued iteratively until a certain level of translation quality is met (we use the BLEU score, WER and PER) (Papineni et al., 2002).

We introduce a novel combined measure of translation quality for multiple target language outputs (the same content from multiple source languages).

However, if we start with only a small amount of initial parallel data for the new target language, then translation quality is very poor and requires a very large injection of human labeled data to be effective.

ting allows new features for active learning which we exploit to improve translation quality while reducing annotation effort.

Figure 5: Maximum translation quality decreasing over similarly scored translation pairs.

reliable for estimating the translation quality at the segment level, for predicting significant improvement between systems and for detecting poor and excellent translations.

The main goal of our work is to analyze to what extent deep linguistic features can contribute to the automatic evaluation of translation quality .

In all cases, translation quality is measured by comparing automatic translations against a set of human references.

We evaluated the translation quality using the BLEU metric, as calculated by mteval-vl lb.pl with its default setting except that we used case-insensitive matching of n-grams.

As a result, packed forests enable tree-to-tree models to capture more useful source-target mappings and therefore improve translation quality .

Studies reveal that the absence of such non-syntactic mappings will impair translation quality dramatically (Marcu et al., 2006; Liu et al., 2007; DeNeefe et al., 2007; Zhang et al., 2008).

They replace l-best trees with packed forests both in training and decoding and show superior translation quality over the state-of-the-art hierarchical phrase-based system.

Our second, more fundamental, strategy replaces the use of loose surrogates of translation quality with a model that attempts to comprehensively assess meaning equivalence between references and MT hypotheses.

(2006) use the degree of overlap between the dependency trees of reference and hypothesis as a predictor of translation quality .

We thus expect even noisy RTE features to be predictive for translation quality .