Improving Back-Transliteration by Combining Information Sources

Transliterating words and names from one language to another is a frequent and highly productive phenomenon. Transliteration is information loosing since important distinctions are not preserved in the process. Hence, automatically converting transliterated words back into their original form is a real challenge. However, due to wide applicability in MT and CLIR, it is a computationally interesting problem. Previously proposed backtransliteration methods are based either on phoneme modeling or grapheme modeling across languages. In this paper, we propose a new method, combining the two models in order to enhance the back– transliterations of words transliterated in Japanese. Our experiments show that the resulting system outperforms singlemodel systems.

• Type: Conference
• Year: 2004
• Where: IJCNLP
• Authors: Slaven Bilac, Hozumi Tanaka
• Keywords: IJCNLP 2004, Important Distinctions, Productive Phenomenon, Transliterated Words