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A Deep Learning Approach to Machine Transliteration

T. Deselaers, S. Hasan, O. Bender, and H. Ney
EACL 2009 Workshop on Statistical Machine Translation
2009

Abstract

In this paper we present a novel transliteration technique which is based on deep belief networks. Common approaches use finite state machines or other methods similar to conventional machine translation. Instead of using conventional NLP techniques, the approach presented here builds on deep belief networks, a technique which was shown to work well for other machine learning problems. We show that deep belief networks have certain properties which are very interesting for transliteration and possibly also for translation and that a combination with conventional techniques leads to an improvement over both components on an Arabic-English transliteration task.


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@InProceedings{eth_biwi_00717,
  author = {T. Deselaers and S. Hasan and O. Bender and and H. Ney},
  title = {A Deep Learning Approach to Machine Transliteration},
  booktitle = {EACL 2009 Workshop on Statistical Machine Translation},
  year = {2009},
  keywords = {}
}