The noisy channel model approach is successfully applied to various natural language processing tasks. Currently the main research focus of this approach is adaptation methods, how to capture characteristics of words and expressions in a target domain given example sentences in that domain. As a solution we describe a method enlarging the vocabulary of a language model to an almost infinite size and capturing their context information. Especially the new method is suitable for languages in which words are not delimited by whitespace. We applied our method to a phoneme-to-text transcription task in Japanese and reduced about 10% of the errors in the results of an existing method.
REF described a method enlarging the vocabulary which can capture the context information.
Phoneme-To-Text Transcription System With An Infinite Vocabulary
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics
Computer Science