o The sense-based translation model is able to substantially improve translation quality in terms of both BLEU and NIST.

To the best of our knowledge, this is the first attempt to empirically verify the positive impact of word senses on translation quality .

Do word senses automatically induced by the HDP-based WSI improve translation quality ?

We evaluated translation quality with the case-insensitive BLEU-4 (Papineni et al., 2002) and NIST (Doddington, 2002).

Our second group of experiments were carried out to investigate whether the sense-base translation model is able to improve translation quality by comparing the system enhanced with our sense-based translation model against the baseline.

They report that WSD degenerates the translation quality of SMT.

With these word senses, we study in particular: 1) whether word senses can be directly integrated to SMT to improve translation quality and 2) whether WSI-based model can outperform the reformulated WSD in the context of SMT.

Results show that automatically learned word senses are able to improve translation quality and the sense-based translation model is better than the previous reformulated WSD.

Our experiments show that such word senses are able to improve translation quality .

We present an adaptive translation quality estimation (QE) method to predict the human-targeted translation error rate (HTER) for a document-specific machine translation model.

However, adding such data in the sub-sampling process extracts more bilingual data for building the MT models, which slightly increase the model building time but increased the translation quality .

As seen in Table 4, we do not notice translation quality degradation.

Machine translation (MT) systems suffer from an inconsistent and unstable translation quality .

It is demonstrated in (Roukos et al., 2012) that document-specific MT models significantly improve the translation quality .

0 The average translation probability of the phrase translation pairs in the final translation, which provides the overall translation quality on the phrase level.

The external features capture the syntactic structure of the source sentence, as well as the coverage of the training data with regard to the input sentence, which are good indicators of the translation quality .

In general, according to formula (3), the translation quality based on the log-linear model is related tightly with the features chosen.

According to the statistics in Table 1, we see that CSS is really widely distributed in the NIST and CWMT corpora, which implies that the translation quality may benefit substantially from the CSS information, if it is well considered in SMT.

To test the effectiveness of the proposed models, we have compared the translation quality of different integration strategies.

From the table, we cannot conclude that the EUC constraint will certainly promote translation quality , but the transfer model performs better with the constraint on most testing sets.

The translation quality is evaluated by case-insensitive IBM BLEU-4 metric.

(2004) proposed a way of using term weight based models in a vector space as additional evidences for phrase pair translation quality .

Recently, many new features have been explored for SMT and significant performance have been obtained in terms of translation quality , such as syntactic features, sparse features, and reordering features.

(2012) improved translation quality of n-gram translation model by using a bilingual neural LM, where translation probabilities are estimated using a continuous representation of translation units in lieu of standard discrete representations.

We investigate this technique in the context of English-to-Arabic and English-to-Finnish translation, showing significant improvements in translation quality over desegmentation of l-best decoder outputs.

7We also experimented on log p(X \Y) as an additional feature, but observed no improvement in translation quality .

We demonstrate that significant improvements in translation quality can be achieved by training a linear model to re-rank this transformed translation space.

This could improve translation quality , as it brings our training scenario closer to our test scenario (test BLEU is always measured on unsegmented references).

Pruning most of the phrase table without much impact on translation quality is very important for translation especially in environments where memory and time constraints are imposed.

We can see a common phenomenon in both of the algorithms: for the first few thresholds, the phrase table becomes smaller and smaller while the translation quality is not much decreased, but the performance jumps a lot at a certain threshold (16 for Significance pruning, 0.8 for BRAE-based one).

As shown in Table 2, no matter What n is, the BRAE model can significantly improve the translation quality in the overall test data.

The experiments show that up to 72% of the phrase table can be discarded without significant decrease on the translation quality , and in decoding with phrasal semantic similarities up to 1.7 BLEU score improvement over the state-of-the-art baseline can be achieved.

The evaluation metric for the overall translation quality is case-insensitive BLEU4 (Papineni et al., 2002).

They reported extensive empirical analysis and improved word alignment accuracy as well as translation quality .

They estimated phrase-topic distributions in translation model adaptation and generated better translation quality .