Experiments on a Chinese to English translation task show that our proposed RZNN can outperform the state-of-the-art baseline by about 1.5 points in BLEU.

We conduct experiments on a Chinese-to-English translation task , and our method outperforms a state-of-the-art baseline about 1.5 points BLEU.

In this section, we conduct experiments to test our method on a Chinese-to-English translation task .

And also, translation task is difference from other NLP tasks, that, it is more important to model the translation confidence directly (the confidence of one

We conduct experiments on a Chinese-to-English translation task to test our proposed methods, and we get about 1.5 BLEU points improvement, compared with a state-of-the-art baseline system.

The RNN-based model outperforms the feed-forward neural network-based model (Yang et al., 2013) as well as the IBM Model 4 under Japanese-English and French-English word alignment tasks, and achieves comparable translation performance to those baselines for Japanese-English and Chinese-English translation tasks .

This paper presents evaluations of Japanese-English and French-English word alignment tasks and Japanese-to-English and Chinese-to-English translation tasks .

For the translation tasks , our model achieves up to 0.74% gain in BLEU as compared to the FFNN-based model, which matches the translation qualities of the IBM Model 4.

In addition, we evaluated the end-to-end translation performance of three tasks: a Chinese-to-English translation task with the FBIS corpus (FBI 8), the IWSLT 2007 Japanese-to-English translation task (I WSLT) (Fordyce, 2007), and the NTCIR-9 Japanese-to-English patent translation task (NTCIR) (Goto et a1., 2011)?

In the translation tasks , we used the Moses phrase-based SMT systems (Koehn et al., 2007).

Experimental results show that our method significantly improves translation accuracy in the NIST Chinese-to-English translation task compared to a state-of-the-art baseline.

We evaluate the performance of our neural network based topic similarity model on a Chinese-to-English machine translation task .

We integrate topic similarity features in the log-linear model and evaluate the performance on the NIST Chinese-to-English translation task .

We evaluate our model on a Chinese to English translation task and obtain up to 1.2 BLEU improvement over strong baselines.

This paper contributes to the deeper integration of topic models into critical applications by presenting a new multilingual topic model, ptLDA, comparing it with other multilingual topic models on a machine translation task , and showing that these topic models improve machine translation.

We explore multiple inference schemes because while all of these methods optimize likelihood because they might give different results on the translation task .

We test the effectiveness of the proposed sense-based translation model on a large-scale Chinese-to-English translation task .

They show that such a reformulated WSD can improve the accuracy of a simplified word translation task .

Section 5 elaborates our experiments on the large-scale Chinese-to-English translation task .

52 different Turkers took part in the translation task , each translating 138 sentences on average.

This suggests that both sources of information— the candidate itself and its authors— are important for the crowdsourcing translation task .

The problem definition of the crowdsourcing translation task is straightforward: given a set of candidate translations for a source sentence, we want to choose the best output translation.