Abstract | Additionally, we remove low confidence alignment links from the word alignment of a bilingual training corpus, which increases the alignment F-score, improves Chinese-English and Arabic-English translation quality and significantly reduces the phrase translation table size. |
Conclusion | lected among multiple alignments and it obtained 0.8 F-measure improvement over the single best Chinese-English aligner. |
Conclusion | When we removed low confidence links from the MaXEnt aligner, we reduced the Chinese-English alignment error by 5% and the Arabic-English alignment error by 10%. |
Improved MaXEnt Aligner with Confidence-based Link Filtering | We applied the confidence-based link filtering on Chinese-English and Arabic-English word alignment. |
Sentence Alignment Confidence Measure | We randomly selected 512 Chinese-English (CE) sentence pairs and generated word alignment using the MaxEnt aligner (Ittycheriah and Roukos, 2005). |
Translation | We evaluate the improved alignment on several Chinese-English and Arabic-English machine translation tasks. |
Translation | In the Chinese-English MT experiment, we selected 40 NW documents, 41 WE documents as the test set, which includes 623 sentences with 16667 words. |
Experimental Results | Speed Ratio 29 142 Chinese-English Objective Hiero SBMT |
Experimental Results | We evaluated on both Chinese-English and Arabic-English translation tasks. |
Experimental Results | For the Chinese-English experiments, we used 260 million words of word-aligned parallel text; the hierarchical system used all of this data, and the syntax-based system used a 65-million word subset. |
Experiments | Table 2 describes the data used for model training in this paper, including the BTEC (Basic Travel Expression Corpus) Chinese-English (CE) corpus and the BTEC English-Spanish (ES) corpus provided by IWSLT 2008 organizers, the HIT olympic CE corpus (2004-863-008)1 and the Europarl ES corpusz. |
Experiments | For Chinese-English translation, we mainly used BTEC CE1 corpus. |
Experiments | We used two commercial RBMT systems in our experiments: System A for Chinese-English bidirectional translation and System B for English-Chinese and English-Spanish translation. |
Conclusion | It proves that our system can work well on the Chinese-English ON translation task. |
Experiments | Compared with the statistical ON translation model, we can see that the performance is improved from 18.29% to 48.71% (the bold data shown in column 1 and column 3 of Table 5) by using our Chinese-English ON translation system. |
Heuristic Query Construction | In order to use the web information to assist Chinese-English ON translation, we must firstly retrieve the bilingual web pages effectively. |
Introduction | For solving these two problems, we propose a Chinese-English organization name translation system using heuristic web mining and asymmetric alignment, which has three innovations. |
Abstract | Experimental results on the NIST MT-2003 Chinese-English translation task show that our method statistically significantly outperforms the four baseline systems. |
Conclusion | Finally, we examine our methods on the FBIS corpus and the NIST MT-2003 Chinese-English translation task. |
Experiment | We evaluate our method on Chinese-English translation task. |
Introduction | We evaluate our method on the NIST MT-2003 Chinese-English translation tasks. |