(2008), i.e, that the Hiero baseline system underperforms compared to the phrase-based system with lexicalized phrase-based reordering for Arabic-English in all test sets, on average by about 0.60 BLEU points (46.13 versus 46.73).

As mentioned in section 2.1, Phrasal-Hiero only uses 48.54% of the rules but achieves as good or even better performance (on average 0.24 BLEU points better) compared to the original Hiero system using the full set of rules.

Table 4 shows that the P.H.+lex system gains on average 0.67 BLEU points (47.04 versus 46.37).

'The transfininafion reduces the out-of—vocabulary (00V) words from 5.2% to 2.6% and gives a gain of 1.87 BLEU points .

adapted parallel data showed an improvement of 1.87 BLEU points over our best baseline.

Using phrase table merging that combined AR and EG’ training data in a way that preferred adapted dialectal data yielded an extra 0.86 BLEU points .

— We built a phrasal Machine Translation (MT) system on adapted EgyptiarflEnglish parallel data, which outperformed a non-adapted baseline by 1.87 BLEU points .

The system trained on AR (B1) performed poorly compared to the one trained on EG (B2) with a 6.75 BLEU points difference.

S], which used only EG’ for training showed an improvement of 1.67 BLEU points from the best baseline system (B4).

The data generated allows us to train a reordering model that gives an improvement of 1.8 BLEU points on the NIST MT—08 Urdu-English evaluation set over a reordering model that only uses manual word alignments, and a gain of 5.2 BLEU points over a standard phrase-based baseline.

Cumulatively, we see a gain of 1.8 BLEU points over a baseline reordering model that only uses manual word alignments, a gain of 2.0 BLEU points over a hierarchical phrase based system, and a gain of 5.2 BLEU points over a phrase based

This results in a 1.8 BLEU point gain in machine translation performance on an Urdu-English machine translation task over a preordering model trained using only manual word alignments.

In all, this increases the gain in performance by using the preordering model to 5.2 BLEU points over a standard phrase-based system with no preordering.

We see a significant gain of 1.8 BLEU points in machine translation by going beyond manual word alignments using the best reordering model reported in Table 3.

We also note a gain of 2.0 BLEU points over a hierarchical phrase based system.

We can see from the table that the domain lexicon is much helpful and significantly outperforms the baseline with more than 4.0 BLEU points .

When it is enhanced with the in-domain language model, it can further improve the translation performance by more than 2.5 BLEU points .

From the results, we see that transductive learning can further improve the translation performance significantly by 0.6 BLEU points .

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

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.

SMT 8.03 BLEU points at interval [0.9, 1.0), while the advantage is only 2.97 BLEU points at interval [0.6, 0.7).

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

Specifically, after integrating the inside context information of PAS into transformation, we can see that system IC-PASTR significantly outperforms system PASTR by 0.71 BLEU points .

Moreover, after we import the MEPD model into system PASTR, we get a significant improvement over PASTR (by 0.54 BLEU points ).

We can see that this system further achieves a remarkable improvement over system PASTR (0.95 BLEU points ).

Adding fertility results in a further +1 BLEU point improvement.

While the baseline results vary by up to 1.7 BLEU points for the different alignments, our Markov model provided more stable results with the biggest difference of 0.6.

The model produces uniformly better translations than those of a competitive phrase-based baseline, amounting to an improvement of up to 3.4 BLEU points absolute.