We achieve significant improvement over both Hiero and phrase-based baselines for Arabic-English, Chinese-English and German-English translation.

We will report the impact of integrating phrase-based features into Hiero systems for three language pairs: Arabic-English, Chinese-English and German-English.

4.3 Chinese-English Results

The Chinese-English system was trained on FBIS corpora of 384K sentence pairs, the English corpus is lower case.

In our Chinese-English experiment, the Hiero system still outperforms the discontinuous phrase-based system.

(2008) added structure distortion features into their decoder and showed improvements in their Chinese-English experiment.

In the experiment section, we will discuss the impact of removing rules with nonaligned sub-phrases in our German-English and Chinese-English experiments.

We evaluate our optimizer on Chinese-English and Arabic-English translation tasks, each with small and large feature sets, and show that our learner is able to achieve significant improvements of 1.2-2 BLEU and 1.7-4.3 TER on average over state-of-the-art optimizers with the large feature set.

In both Arabic-English feature sets, MIRA seems to take the second place, while RAMPION lags behind, unlike in Chinese-English (§4).6

Spread analysis: For RM, the average spread of the projected data in the Chinese-English small feature set was 0.9i3.6 for all tuning iterations, and 0.7j:2.9 for the iteration with the highest decoder performance.

To evaluate the advantage of explicitly accounting for the spread of the data, we conducted several experiments on two Chinese-English translation test sets, using two different feature sets in each.

As can be seen from the results in Table 3, our RM method was the best performer in all Chinese-English tests according to all measures — up to 1.9 BLEU and 6.6 TER over MIRA — even though we only optimized for BLEU.5 Surprisingly, it seems that MIRA did not benefit as much from the sparse features as RM.

In preliminary experiments with a smaller trigram LM, our RM method consistently yielded the highest scores in all Chinese-English tests — up to 1.6 BLEU and 6.4 TER from MIRA, the second best performer.

Chinese-English translation experiments show that our algorithm, RM, significantly outperforms strong state-of-the-art optimizers, in both a basic feature setting and high-dimensional (sparse) feature space (§4).

Experiments on Chinese-English translation demonstrated the effectiveness of our approach on enhancing the quality of overall translation, name translation and word alignment over a high-quality MT baselinel.

As our baseline, we apply a high-performing Chinese-English MT system (Zheng, 2008; Zheng et al., 2009) based on hierarchical phrase-based translation framework (Chiang, 2005).

Experiments on Chinese-English translation demonstrated the effectiveness of our approach over a high-quality MT baseline in both overall translation and name translation, especially for formal genres.

We used a large Chinese-English MT training corpus from various sources and genres (including newswire, web text, broadcast news and broadcast conversations) for our experiments.

get side of Chinese-English and Egyptian Arabic-English parallel text, English monolingual discussion forums data Rl-R4 released in BOLT Phase 1 (LDC2012E04, LDC2012E16, LDC2012E21, LDC2012E54), and English Gigaword Fifth Edition (LDC2011T07).

We conducted the experiment on the Chinese-English Parallel Treebank (Li et a1., 2010) with ground-truth word alignment.

We have been able to extract over 1M Chinese-English parallel segments from Sina Weibo (the Chinese counterpart of Twitter) using only their public APIs.

For the news test, we created a new test set from a crawl of the Chinese-English documents on the Project Syndicate website2, which contains news commentary articles.

Second, we use the full 2012 NIST Chinese-English dataset (approximately 8M sentence pairs, including FBIS).

Section 5 describes the data we gathered from both Sina Weibo (Chinese-English) and Twitter ( Chinese-English and Arabic-English).

The target domains in this work are Twitter and Sina Weibo, and the main language pair is Chinese-English .

This means that for the Chinese-English language pair, we only keep tweets with more than 3 Mandarin characters and 3 latin words.

The first experiments are on the IWSLT data set for Chinese-English translation.

Table 3: Machine translation performance in BLE U % on the IWSLT 2005 Chinese-English test set.

To test whether our improvements carry over to larger datasets, we assess the performance of our model on the FBIS Chinese-English data set.

We demonstrate our model on Chinese-English and Arabic-English translation datasets.

As our approach combines the merits of phrase-based and string-to-dependency models, it achieves significant improvements over the two baselines on the NIST Chinese-English datasets.

We evaluate our method on the NIST Chinese-English translation datasets.

1Empirically, we find that the average number of stacks for J words is about 1.5 X J on the Chinese-English data.

We evaluated our phrase-based string-to-dependency translation system on Chinese-English translation.

In our experiments, our model improved 2.9 BLEU points for J apanese-English and 2.6 BLEU points for Chinese-English translation compared to the lexical reordering models.

Japanese-English Chinese-English HIER 30.47 32.66

Experiments confirmed the effectiveness of our method for J apanese-English and Chinese-English translation, using NTCIR-9 Patent Machine Translation Task data sets (Goto et al., 2011).

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

We built Arabic-English and Chinese-English MT systems with Phrasal (Cer et al., 2010), a phrase-based system based on alignment templates (Och and Ney, 2004).

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

The system is trained on 10 million parallel sentences that are available to the Phase 1 of the DARPA BOLT Chinese-English MT task.

For our Chinese-English experiments, we use a simple heuristic that equates as anchors, single-word chunks whose corresponding word class belongs to closed-word classes, bearing a close resemblance to (Setiawan et al., 2007).

Figure 1: An aligned Chinese-English sentence pair.

Chinese-English experimental results on four typical attributes showed that WikiCiKE significantly outperforms both the current translation based methods and the monolingual extraction methods.

In this section, we present our experiments to evaluate the effectiveness of WikiCiKE, where we focus on the Chinese-English case; in other words, the target language is Chinese and the source language is English.

Chinese-English experiments for four typical attributes demonstrate that WikiCiKE outperforms both the monolingual extraction method and current translation-based method.