This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication

Year(s) from:  to 
Keywords (separated by spaces):

A Deep Learning Approach to Machine Transliteration

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


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.

Download in pdf format
  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 = {}