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
2075241709 2019614587 128271544 1563762850
We present in this paper the method of English-to-Korean (E-K) transliteration and back-transliteration. In Korean technical documents, many English words are transliterated into Korean words in various forms in diverse ways. As English words and Korean transliterations are usually technical terms and proper nouns, it is hard to find a transliteration and its variations in a dictionary. Therefore an automatic transliteration system is needed to find the transliterations of English words without manual intervention.To explain E-K transliteration phenomena, we use phoneme chunks that do not have a length limit. By applying phoneme chunks, we combine different length information with easy. The E-K transliteration method has three steps. In the first, we make a phoneme network that shows all possible transliterations of the given word. In the second step, we apply phoneme chunks, extracted from training data, to calculate the reliability of each possible transliteration. Then we obtain probable transliterations of the given English word. Most foreign names are transliterated into Chinese, Japanese or Korean with approximate phonetic equivalents. The transliteration is usually achieved through intermediate phonemic mapping. This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM). With the n-gram TM model, we automate the orthographic alignment process to derive the aligned transliteration units from a bilingual dictionary. The n-gram TM under the DOM framework greatly reduces system development effort and provides a quantum leap in improvement in transliteration accuracy over that of other state-of-the-art machine learning algorithms. The modeling framework is validated through several experiments for English-Chinese language pair. In Korean technical documents, many English words are transliterated into Korean in various ways. Most of these words are technical terms and proper nouns that are frequently used as query terms in information retrieval systems. As the communication with foreigners increases, an automatic transliteration system is needed to find the various transliterations for the cross lingual information systems, especially for the proper nouns and technical terms which are not registered in the dictionary. In this paper, we present a language independent Statistical Transliteration Model (STM) that learns rules automatically from word-aligned pairs in order to generate transliteration variations. For the transliteration from English to Korean, we compared two methods based on STM: the pivot method and the direct method. In the pivot method, the transliteration is done in two steps: converting English words into pronunciation symbols by using the STM and then converting these symbols into Korean words by using the Korean standard conversion rule. In the direct method, English words are directly converted to Korean words by using the STM without intermediate steps. After comparing the performance of the two methods, we propose a hybrid method that is more effective to generate various transliterations and consequently to retrieve more relevant documents. A proper noun dictionary is never complete rendering name translation from English to Chinese ineffective. One way to solve this problem is not to rely on a dictionary alone but to adopt automatic translation according to pronunciation similarities, i.e. to map phonemes comprising an English name to the phonetic representations of the corresponding Chinese name. This process is called transliteration. We present a statistical transliteration method. An efficient algorithm for aligning phoneme chunks is described. Unlike rule-based approaches, our method is data-driven. Compared to source-channel based statistical approaches, we adopt a direct transliteration model, i.e. the direction of probabilistic estimation conforms to the transliteration direction. We demonstrate comparable performance to source-channel based system.