This paper introduces a novel pre-ordering approach based on dependency parsing for Chinese-English SMT.

In this paper, we introduced a novel pre-ordering approach based on dependency parsing for a Chinese-English PBSMT system.

These results indicated that dependency parsing is more effective for conducting pre-ordering for Chinese-English PBSMT.

Our development set was the official NIST MT evaluation data from 2002 to 2005, consisting of 4476 Chinese-English sentences pairs.

Syntax-based pre-ordering by employing constituent parsing have demonstrated effectiveness in many language pairs, such as English-French (Xia and McCord, 2004), German-English (Collins et al., 2005), Chinese-English (Wang et al., 2007; Zhang et al., 2008), and English-Japanese (Lee et al., 2010).

The purpose of this paper is to introduce a novel dependency-based pre-ordering approach through creating a pre-ordering rule set and applying it to the Chinese-English PBSMT system.

To our knowledge, our manually created pre-ordering rule set is the first Chinese-English dependency-based pre-ordering rule set.

We also show strong improvements on the NIST OpenMT12 Chinese-English task, as well as the DARPA BOLT (Broad Operational Language Translation) Arabic-English and Chinese-English conditions.

We present MT primary results on Arabic-English and Chinese-English for the NIST OpenMT12 and DARPA BOLT conditions.

Table 3: Primary results on Arabic-English and Chinese-English NIST MT12 Test Set.

For the Chinese-English condition, there is an improvement of +0.8 BLEU from the primary NNJM and +1.3 BLEU overall.

An example of the NNJ M context model for a Chinese-English parallel sentence is given in Figure 1.

On two Chinese-English tasks, our semi-supervised DAE features obtain statistically significant improvements of l.34/2.45 (IWSLT) and 0.82/1.52 (NIST) BLEU points over the unsupervised DBN features and the baseline features, respectively.

We now test our DAE features on the following two Chinese-English translation tasks.

The bilingual corpus is the Chinese-English part of Basic Traveling Expression corpus (BTEC) and China-Japan-Korea (CJK) corpus (0.38M sentence pairs with 3.5/3.8M Chi-nese/English words).

Finally, we conduct large-scale experiments on IWSLT and NIST Chinese-English translation tasks, respectively, and the results demonstrate that our solutions solve the two aforementioned shortcomings successfully.

In this work we consider two types of code-switched documents: single messages and conversations, and two language pairs: Chinese-English and Spanish-English.

An example of a Chinese-English code-switched messages is given by Ling et al.

We used two datasets: a Sina Weibo Chinese-English corpus (Ling et al., 2013) and a Spanish-English Twitter corpus.

We apply our approach to a state-of-the-art phrase-based system and demonstrate very promising BLEU improvements and TER reductions on the NIST Chinese-English MT evaluation data.

We experimented with our approach on Chinese-English translation using the NiuTrans open-source MT toolkit (Xiao et al., 2012).

It contains the annotation of sentence skeleton on the Chinese-language side of the Penn Parallel Chinese-English Treebank (LD-C2003E07).

0 We apply the proposed model to Chinese-English phrase-based MT and demonstrate promising BLEU improvements and TER reductions on the NIST evaluation data.

Experiments on Chinese-English translation show that the reordering approach can significantly improve a state-of-the-art hierarchical phrase-based translation system.

Experiments on Chinese-English translation show that the reordering approach can significantly improve a state-of-the-art hierarchical phrase-based translation system.

In this section, we test its effectiveness in Chinese-English translation.