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A Comparison of Different Machine Transliteration Models

机译:不同机器音译模型的比较

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摘要

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.
机译:机器音译是一种自动将一种语言的单词转换为另一种语言的语音等效单词的方法。机器音译在自然语言应用(例如信息检索和机器翻译)中起着重要作用,尤其是在处理专有名词和技术术语方面。几个研究人员提出了四种机器音译模型-基于字素的音译模型,基于音素的音译模型,混合音译模型和基于对应的音译模型。但是,迄今为止,对可以同时运行多个音译模型的框架的研究很少。此外,在相同框架内和使用相同数据的情况下,没有四个模型的比较。我们通过以下方法解决了这些问题:1)在相同框架内对四个模型进行建模; 2)在相同条件下进行比较;以及3)通过此比较开发一种改进机器音译的方法。我们的比较表明,混合模型和基于对应关系的模型最为有效,并且可以互补使用这四个模型来提高机器音译性能。

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