Machine Transliteration Using SVM and HMM

Abstract

Name transliteration plays an important role in developing automatic machine translation and cross lingual information retrieval system because these systems cannot directly translate out-of-vocabulary (OOV) words. In this article, a SVM-based name transliteration approach has been presented. This approach considers transliteration task as a multi-class problem of pattern classification, where the input is a source transliteration unit (chunks of source grapheme) and the classes are the distinct transliteration units (chunks of target grapheme) in the target language. A study on using hidden Markov model (HMM) for solving machine transliteration problem viewed as a sequence learning problem has also been presented in this paper. Bengali-to-English forward and backward name transliteration have been considered in this study. Our proposed methods have been compared with some existing transliteration method that uses a modified version of joint-source channel model. After the systems have been evaluated, the obtained results show that our proposed SVM-based model gives the best results among the others. Our experiments also reveal that the performance of HMM-based system is comparable with the SVM-based system.