Index of papers in Proc. ACL 2009 that mention
  • Chinese-English
Huang, Fei
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.
Chinese-English is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
DeNero, John and Chiang, David and Knight, Kevin
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.
Chinese-English is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Wu, Hua and Wang, Haifeng
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.
Chinese-English is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Yang, Fan and Zhao, Jun and Liu, Kang
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.
Chinese-English is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Zhang, Hui and Zhang, Min and Li, Haizhou and Aw, Aiti and Tan, Chew Lim
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.
Chinese-English is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: