Abstract

Machine Transliteration deals with the conversion of text strings from one orthography to another, while preserving the phonetics of the strings in the two languages. Transliteration is an important problem in machine translation or cross-lingual information retrieval, as most proper names and generic iconic terms are out-of-vocabulary words, and therefore need to be transliterated. In this demo, we present Babel, a transliteration workbench, with generic statistical learning algorithms and a scripting engine to model the transliteration process. We demonstrate quick assembly of necessary components – algorithmic modules and training scripts – for systematic experimentation of transliteration tasks in a given pair of languages.