Abstract
We present a method for performing machine transliteration without any parallel resources. We frame the transliteration task as a decipherment problem and show that it is possible to learn cross-language phoneme mapping tables using only monolingual resources. We compare various methods and evaluate their accuracies on a standard name transliteration task. © 2009 Association for Computational Linguistics.
Cite
CITATION STYLE
Ravi, S., & Knight, K. (2009). Learning phoneme mappings for transliteration without parallel data. In NAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 37–45). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1620754.1620761
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