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Integrating Multi-source Bilingual Information for Chinese Word Segmentation in Statistical Machine Translation
Chen W(陈炜); Wei W(韦玮); Chen ZB(陈振标); Xu B(徐波); Chen,Wei
2013-10
Conference NameChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data.(CCL)
Source PublicationChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data.(CCL)
Conference Date2013-10
Conference PlaceSuzhou,China
AbstractChinese texts are written without spaces between the words, which is problematic for Chinese-English statistical machine translation (SMT). The most widely used approach in existing SMT systems is apply a fixed segmentations produced by the off-the-shelf Chinese word segmentation (CWS) systems to train the standard translation model. Such approach is sub-optimal and unsuitable for SMT systems. We propose a joint model to integrate the multi-source bilingual information to optimize the segmentations in SMT. We also propose an unsupervised algorithm to improve the quality of the joint model iteratively. Experiments show that our method improve both segmentation and translation performance in different data environment.
KeywordChinese Word Segmentation Bilingual Information Statistical Machine Translation
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11805
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorChen,Wei
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Chen W,Wei W,Chen ZB,et al. Integrating Multi-source Bilingual Information for Chinese Word Segmentation in Statistical Machine Translation[C],2013.
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