For instance, noisy-channel model (NCM) (Virga et al., 2003;Lee et al., 2003), HMM (Sung et al., 2000), decision tree (Kang et al., 2000), transformation-based learning (Meng et al., 2001), statistical machine transliteration model (Lee et al., 2003), finite state transducers (Knight et al., 1998) and rule-based approach (Wan et al., 1998;Oh et al., 2002).
it is observed that the reported transliteration models share a common strategy, that is: 1) To model the transformation rules; 2) To model the target language; 3) To model the above both; the modeling of different knowledge is always done independently.