CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Approaches to Improving Corpus Quality for Statistical Machine Translation
Zhou, Yu; Liu, Peng; Zong, Chengqing
Source PublicationInternational Journal of Computer Processing of Oriental Languages (IJCPOL)
2011
Volume23Issue:4Pages:327-348
AbstractThe performance of a statistical machine translation (SMT) system heavily depends on the quantity and quality of the bilingual language resource. However, the pervious work mainly focuses on the quantity and tries to collect more bilingual data. In this paper, we aim to optimize the bilingual corpus to improve the performance of the translation system. We propose methods to process the bilingual language data by filtering noise and selecting more informative sentences from the training corpus and the development corpus. The experimental results show that we can obtain a competitive performance using less data compared with using all available data.
KeywordMachine Translation Corpus Quality
Indexed ByEI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21768
Collection模式识别国家重点实验室_自然语言处理
Recommended Citation
GB/T 7714
Zhou, Yu,Liu, Peng,Zong, Chengqing. Approaches to Improving Corpus Quality for Statistical Machine Translation[J]. International Journal of Computer Processing of Oriental Languages (IJCPOL),2011,23(4):327-348.
APA Zhou, Yu,Liu, Peng,&Zong, Chengqing.(2011).Approaches to Improving Corpus Quality for Statistical Machine Translation.International Journal of Computer Processing of Oriental Languages (IJCPOL),23(4),327-348.
MLA Zhou, Yu,et al."Approaches to Improving Corpus Quality for Statistical Machine Translation".International Journal of Computer Processing of Oriental Languages (IJCPOL) 23.4(2011):327-348.
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