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
2011
发表期刊International Journal of Computer Processing of Oriental Languages (IJCPOL)
卷号23期号:4页码:327-348
摘要The 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.
关键词Machine Translation Corpus Quality
收录类别EI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21768
专题模式识别国家重点实验室_自然语言处理
推荐引用方式
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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