Segmentation is typically applied as a bitext preprocessing step, and there is a rich literature on the effect of different segmentation schemata on translation quality (Koehn and Knight, 2003; Habash and Sadat, 2006; El Kholy and Habash, 2012).

Our class-based agreement model improves translation quality by promoting local agreement, but with a minimal increase in decoding time and no additional storage requirements for the phrase table.

2 shows translation quality results on newswire, while Tbl.

We first evaluate the Arabic segmenter and tagger components independently, then provide English-Arabic translation quality results.

5.2 Translation Quality

We evaluated translation quality with BLEU-4 (Pa-pineni et al., 2002) and computed statistical significance with the approximate randomization method of Riezler and Maxwell (2005).9

However, using lexical coverage experiments, we show that there is ample room for translation quality improvements through better selection of forms that already exist in the translation model.

We explain how to implement this extension efliciently for large-scale data (also released as a modification to GIZA++) and demonstrate, in experiments on Czech, Arabic, Chinese, and Urdu to English translation, significant improvements over IBM Model 4 in both word alignment (up to +6.7 F1) and translation quality (up to +1.4 B ).

The method is implemented as a modification to the open-source toolkit GIZA++, and we have shown that it significantly improves translation quality across four different language pairs.

As we will see below, we still obtained strong improvements in translation quality when hand-aligned data was unavailable.

We then tested the effect of word alignments on translation quality using the hierarchical phrase-based translation system Hiero (Chiang, 2007).

We ran some contrastive experiments to investigate the impact of hyperparameter tuning on translation quality .

In this paper, we propose a simple extension to the IBM/HMM models that is unsupervised like the IBM models, is as scalable as GIZA++ because it is implemented on top of GIZA++, and provides significant improvements in both alignment and translation quality .

Experiments on Czech-, Arabic-, Chinese- and Urdu-English translation (Section 3) demonstrate consistent significant improvements over IBM Model 4 in both word alignment (up to +6.7 F1) and translation quality (up to +1.4 B ).

Our results show that language model based pre-sorting yields a small improvement in translation quality and a speedup by a factor of 2.

We compare our approach with Moses and observe the same performance, but a substantially better tradeoff between translation quality and speed.

We compare our decoder to Moses, reaching a similar highest BLEU score, but clearly outperforming it in terms of scalability with respect to the tradeoff ratio between translation quality and speed.

It yields nearly the same top performance with an even better tradeoff between translation quality and speed.

phrase translation candidates has a positive effect on both translation quality and speed.

A better pre-selection can be expected to improve translation quality .

BLEU, TER) focus on different aspects of translation quality ; our multi-objective approach leverages these diverse aspects to improve overall quality.

These methods are effective because they tune the system to maximize an automatic evaluation metric such as BLEU, which serve as surrogate objective for translation quality .

Ideally, we want to tune towards an automatic metric that has perfect correlation with human judgments of translation quality .

Different evaluation metrics focus on different aspects of translation quality .

These rules are employed to enrich the SMT inputs for translation quality improvement.

The manual investigation on oral translation results indicate that the paraphrase rules capture four kinds of MT-favored transformation to ensure translation quality improvement.

investigate What kinds of transformation finally lead to the translation quality improvement.

Finally the translation quality of Back-Translation is evaluated by using the original source texts as references.

The translation quality of the SMT system is highly related to the coverage of translation models.

Is our topic similarity model able to improve translation quality in terms of BLEU?

This verifies that topic similarity model can improve the translation quality significantly.

To exploit topic information for statistical machine translation (SMT), researchers have proposed various topic-specific lexicon translation models (Zhao and Xing, 2006; Zhao and Xing, 2007; Tam et al., 2007) to improve translation quality .

We further show that both the source-side and target-side topic distributions improve translation quality and their improvements are complementary to each other.

Table 4 shows translation quality for BLEU- and PORT-tuned systems, as assessed by automatic metrics.

Many of the metrics correlate better with human judgments of translation quality than BLEU, as shown in recent WMT Evaluation Task reports (Callison-Burch et

Results given below show that PORT correlates better with human judgments of translation quality than BLEU does, and sometimes outperforms METEOR in this respect, based on data from WMT (2008—2010).

Therefore, it is desirable to train all these parameters to directly maximize an objective that directly links to translation quality .

The training objective is an expected BLEU score, which is closely linked to translation quality .

The expected BLEU score is closely linked to translation quality and the regularization is essential when many parameters are trained at scale.