Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models - grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model - have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.
Knight and Graehl knight97 modeled Japanese-to-English transliteration with weighted finite state transducers (WFSTs) by combining several parameters including romaji-to-phoneme, phoneme-to-English, English word probabilities, and so on. A similar model was developed for Arabic-to-English transliteration @cite_4 . Meng meng01 proposed an English-to-Chinese transliteration method based on English grapheme-to-phoneme conversion, cross-lingual phonological rules, mapping rules between English phonemes and Chinese phonemes, and Chinese syllable-based and character-based language models. Jung jung00 modeled English-to-Korean transliteration with an extended Markov window. The method transforms an English word into English pronunciation by using a pronunciation dictionary. Then it segments the English phonemes into chunks of English phonemes; each chunk corresponds to a Korean grapheme as defined by handcrafted rules. Finally, it automatically transforms each chunk of English phonemes into Korean graphemes by using an extended Markov window.
@cite_4
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It is challenging to translate names and technical terms from English into Arabic. Translation is usually done phonetically: different alphabets and sound inventories force various compromises. For example, Peter Streams may come out as [Abstract contained text which could not be captured.] bytr strymz. This process is called transliteration. We address here the reverse problem: given a foreign name or loanword in Arabic text, we want to recover the original in Roman script. For example, an input like [Abstract contained text which could not be captured.] bytr strymz should yield an output like Peter Streams. Arabic presents special challenges due to unwritten vowels and phonetic-context effects. We present results and examples of use in an Arabic-to-English machine translator.