We incrementally explore capturing various syntactic substructures as complex tags on the English side, and evaluate how our translations improve in BLEU scores .

Wherever meaningful, we report the average BLEU scores over 10 data sets along with the maximum and minimum values and the standard deviation.

Table 1: BLEU scores for a variety of transformation combinations

15Note than in this case, the translations would be generated in the same format, but we then split such postpositions from the words they are attached to, during decoding, and then evaluate the BLEU score .

We find that with the full set of syntax-to-morphology transformations and some additional techniques we can get about 39% relative improvement in BLEU scores over a word-based baseline and about 28% improvement of a factored baseline, all experiments being done over 10 training and test sets.

We find (and elaborate later) that this reduction in the English side of the training corpus, in general, is about 30%, and is correlated with improved BLEU scores .

As compared to baseline systems, we achieve absolute improvements of 2.40 BLEU score on a phrase-based SMT system and 1.76 BLEU score on a parsing-based SMT system.

The improved word alignment results in an improvement of 2.16 BLEU score on a phrase-based SMT system and an improvement of 1.76 BLEU score on a parsing-based SMT system.

When we also used phrase collocation probabilities as additional features, the phrase-based SMT performance is finally improved by 2.40 BLEU score as compared with the baseline system.

The system using the improved word alignments achieves an absolute improvement of 1.76 BLEU score , which indicates that the improvements of word alignments are also effective to improve the performance of the parsing-based SMT systems.

If the same alignment method is used, the systems using CM-3 got the highest BLEU scores .

When the phrase collocation probabilities are incorporated into the SMT system, the translation quality is improved, achieving an absolute improvement of 0.85 BLEU score .

As compared with the baseline system, an absolute improvement of 2.40 BLEU score is achieved.

The alignment improvement results in an improvement of 2.16 BLEU score on phrase-based SMT system and an improvement of 1.76 BLEU score on parsing-based SMT system.

SMT performance is further improved by 0.24 BLEU score .

Using all constituency-to-dependency translation rules and bilingual phrases, our model achieves +0.7 points improvement in BLEU score significantly over a state-of-the-art forest-based tree-to-string system.

We use the standard minimum error-rate training (Och, 2003) to tune the feature weights to maximize the system’s BLEU score on development set.

The baseline system extracts 31.9M 625 rules, 77.9M 525 rules respectively and achieves a BLEU score of 34.17 on the test set3.

As shown in the third line in the column of BLEU score , the performance drops 1.7 BLEU points over baseline system due to the poorer rule coverage.

where BLEU(e,-j, r,-) is the smoothed sentence-level BLEU score (Liang et al., 2006) of the translation e with respect to the reference translations r,, and e: is the oracle translation which is selected from {em ..., em} in terms of BLEU(e,-j, r,-).

Figures 2-5 show the BLEU curves on the development and test sets, where the X-aXis is the iteration number, and the Y-aXis is the BLEU score of the system generated by the boosting-based system combination.

The BLEU scores tend to converge to the stable values after 20 iterations for all the systems.

On top of the pruning framework, we also propose a discriminative ITG alignment model using hierarchical phrase pairs, which improves both F-score and Bleu score over the baseline alignment system of GIZA++.

Finally, we also do end-to-end evaluation using both F-score in alignment and Bleu score in translation.

HP-DITG using DPDI achieves the best Bleu score with acceptable time cost.

It shows that HP-DITG (with DPDI) is better than the three baselines both in alignment F-score and Bleu score .

Evaluated in French by 10-fold-cross validation, the system achieves a 9.3% Word Error Rate and a 0.83 BLEU score .

Evaluated by tenfold cross-validation, the system seems efficient, and the performance in terms of BLEU score and WER are quite encouraging.

The system was evaluated in terms of BLEU score (Papineni et al., 2001), Word Error Rate (WER) and Sentence Error Rate (SER).

The copy-paste results just inform about the real deViation of our corpus from the traditional spelling conventions, and highlight the fact that our system is still at pains to significantly reduce the SER, while results in terms of WER and BLEU score are quite encouraging.

This is confirmed for other languages as well: the lower the BLEU score the lower the correlation to human judgments.

We plot the official BLEU score against the rank established as the percentage of sentences where a system ranked no worse than all its competitors (Callison-Burch et al., 2009).

Figure 3 documents the issue across languages: the lower the BLEU score itself (i.e.

A phrase-based system like Moses (cu-bojar) can sometimes produce a long sequence of tokens exactly as required by the reference, leading to a high BLEU score .

We obtain final BLEU scores of 19.35 (conditional probability model) and 19.00 (joint probability model) as compared to 14.30 for a baseline phrase-based system and 16.25 for a system which transliterates OOV words in the baseline system.

This section shows the improvement in BLEU score by applying heuristics and combinations of heuristics in both the models.

For other parts of the data where the translators have heavily used transliteration, the system may receive a higher BLEU score .

Section 4 discusses the training data, parameter optimization and the initial set of experiments that compare our two models with a baseline Hindi-Urdu phrase-based system and with two transliteration-aided phrase-based systems in terms of BLEU scores

Statistical significance in BLEU score differences was tested by paired bootstrap re-sampling (Koehn, 2004).

Best ESSP (Wchpwen) is significantly better than baseline (p<0.0l) in BLEU score, best SMP (wdpwen) is significantly better than baseline (p<0.05) in BLEU score .

wchpwen is significantly better than baseline (p<0.04) in BLEU score .

Our model outperforms a GIZA++ Model-4 baseline by 6.3 points in F-measure, yielding a 1.1 BLEU score increase over a state-of-the-art syntax-based machine translation system.

We treat word alignment as a parsing problem, and by taking advantage of English syntax and the hypergraph structure of our search algorithm, we report significant increases in both F-measure and BLEU score over standard baselines in use by most state-of-the-art MT systems today.

Very recent work in word alignment has also started to report downstream effects on BLEU score .

We perform minimum error rate training with the downhill simplex algorithm (Nelder and Mead, 1965) on the development data to obtain a set of scaling factors that achieve a good BLEU score .

A second iteration of the training algorithm shows nearly no changes in BLEU score , but a small improvement in TER.

yields a BLEU score slightly lower than with fixed interpolation on both DEV and TEST.