Abstract
Machine transliteration is an important Natural Language Processing task. This paper proposes a Noisy Channel Model for Grapheme-based machine transliteration. Moses, a phrase-based Statistical Machine Translation tool, is employed for the implementation of the system. Experiments are carried out on the NEWS 2009 Machine Transliteration Shared Task English-Chinese track. English-Chinese back transliteration is studied as well.
Cite
CITATION STYLE
Jia, Y., Zhu, D., & Yu, S. (2009). A noisy channel model for Grapheme-based machine transliteration. In NEWS 2009 - 2009 Named Entities Workshop: Shared Task on Transliteration at the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009 (pp. 88–91). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1699705.1699728
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