A manual evaluation of an English-to-German translation task shows that the subcategorization information has a positive impact on translation quality through better prediction of case.

We showed in a manual evaluation that the proposed features have a positive impact on translation quality .

We also present a manual evaluation of our best system which shows that the new features improve translation quality .

While the inflection prediction systems (1-4) are significantly12 better than the surface-form system (0), the different versions of the inflection systems are not distinguishable in terms of BLEU; however, our manual evaluation shows that the new features have a positive impact on translation quality .

Integrating semantic role information into SMT has been demonstrated by various researchers to improve translation quality (cf.

This outcome, in particular that adding the lemma of the preposition to the PP node helps to improve translation quality , has been observed before in tree restructuring work for improving translation (Huang and Knight, 2006).

3 Preliminary experiments showed that larger windows do not improve translation quality .

Recent discriminative algorithms that accommodate sparse features have produced smaller than expected translation quality gains in large systems.

Large-scale experiments on Arabic-English and Chinese-English show that our method produces significant translation quality gains by exploiting sparse features.

Tables 2 and 3 show that adding tuning examples improves translation quality .

(2012) showed significant translation quality gains by tuning on the bitext.

When tuned on bitext5k the translation quality gains are significant for bitextSk-test relative to tuning on MT05/ 6/ 8, which has multiple references.

We conduct large-scale translation quality experiments on Arabic-English and Chinese-English.

We measured the overall translation quality with the help of 4-gram BLEU (Papineni et al., 2002), which was computed on tokenized and lower-cased data for both systems.

(2006) use syntactic annotations on the source language side and show significant improvements in translation quality .

(2009), and Chiang (2010), the integration of syntactic information on both sides tends to decrease translation quality because the systems become too restrictive.

The translation quality is automatically measured using BLEU scores, and we confirm the findings by providing linguistic evidence (see Section 5).

Our experiments on two machine translation quality estimation datasets show uniform significant accuracy gains from multitask learning, and consistently outperform strong baselines.

Our experiments showed how our approach outperformed competitive baselines on two machine translation quality regression problems, including the highly challenging problem of predicting post-editing time.

In addition to annotators’ own perceptions and expectations with respect to translation quality , a number of factors can affect their judgements on specific sentences.

We show in our experiments on two translation quality datasets that these multitask learning strategies are far superior to training individual per-task models or a single pooled model, and moreover that our multitask learning approach can achieve similar performance to these baselines using only a fraction of the training data.

Additionally, we also propose a new MT metric to appropriately evaluate the translation quality of informative words, by assigning different weights to different words according to their importance values in a document.

Furthermore, using external name translation table only did not improve translation quality in most test sets except for BOLT2.

Although the proposed model has significantly enhanced translation quality , some challenges remain.

In order to properly evaluate the translation quality of NAMT methods, we propose to modify the BLEU metric so that they can dynamically assign more weights to names during evaluation.

Experiments show that our approach helps to achieve significant improvements on translation quality .

The translation quality is evaluated by case-insensitive BLEU-4 with shortest length penalty.

This harms the translation quality .

By incorporating the rich context information as features, they chose better rules for translation and yielded stable improvements on translation quality .

We apply our method for the domain adaptation task and the extensive experiments show that our proposed method can substantially improve the translation quality .

Our purpose is to induce phrase pairs to improve translation quality for domain adaptation.

some good methods to explore the potential of the given data to improve the translation quality .