Compositional Machine Transliteration

Abstract

Machine transliteration is an important problem in an increasingly multilingual world, as it plays a critical role in many downstream applications, such as machine translation or crosslingual information retrieval systems. In this article, we propose compositional machine transliteration systems, where multiple transliteration components may be composed either to improve existing transliteration quality, or to enable transliteration functionality between languages even when no direct parallel names corpora exist between them. Specifically, we propose two distinct forms of composition: serial and parallel. Serial compositional system chains individual transliteration components, say, X → Y and Y → Z systems, to provide transliteration functionality, X → Z. In parallel composition evidence from multiple transliteration paths between X → Z are aggregated for improving the quality of a direct system. We demonstrate the functionality and performance benefits of the compositional methodology using a state-of-the-art machine transliteration framework in English and a set of Indian languages, namely, Hindi, Marathi, and Kannada. Finally, we underscore the utility and practicality of our compositional approach by showing that a CLIR system integrated with compositional transliteration systems performs consistently on par with, and sometimes better than, that integrated with a direct transliteration system.