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
会议名称Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data.(CCL)
会议录名称Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data.(CCL)
会议日期2013-10
会议地点Suzhou,China
摘要Chinese 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.
关键词Chinese Word Segmentation Bilingual Information Statistical Machine Translation
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11805
专题数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者Chen,Wei
作者单位中国科学院自动化研究所
推荐引用方式
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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