A Transformation-Based Learning Method on Generating Korean Standard Pronunciation

A Transformation-Based Learning Method on Generating Korean Standard Pronunciation* Kim Dong-Sung a and Chang-Hwa Roh a a Department of Linguistics and Cognitive Science Hankuk University of Foreign Studies San89 Wansanri Mohyunmeon Yonginsi, Kyunggido Korea {dsk202, rayr}@hufs.ac.kr Abstract. In this paper, we propose a Transformation-Based Learning (TBL) method on generating the Korean standard pronunciation. Previous studies on the phonological processing have been focused on the phonological rule applications and the finite state automata (Johnson 1984; Kaplan and Kay 1994; Koskenniemi 1983; Bird 1995). In case of Korean computational phonology, some former researches have approached the phonological rule based pronunciation generation system (Lee et al. 2005; Lee 1998). This study suggests a corpus-based and data-oriented rule learning method on generating Korean standard pronunciation. In order to substituting rule-based generation with corpus- based one, an aligned corpus between an input and its pronunciation counterpart has been devised. We conducted an experiment on generating the standard pronunciation with the TBL algorithm, based on this aligned corpus. Keywords: Transformation-Based Learning, Computational Phonology, Data-oriented Processing, Corpus-based Learning, Pronunciation Generation 1. Introduction This paper presents a Transformation-Based Learning (TBL) method on generating Korean standard pronunciation. Previous studies on the phonological processing have been focused on the computation of the phonological rule application and the representation of the finite state automata (Johnson 1984; Kaplan and Kay 1994; Koskenniemi 1983; Bird 1995). In case of Korean computational phonology, some former researches have approached the pronunciation generation based on the phonological rules (Lee et al. 2005; Lee 1998)1. Unlike previous works, this study suggests a standard Korean pronunciation generation method on the basis of corpus- based and data-oriented TBL learning. The role of the computational phonology is to generate a legitimate output counterpart of the underlying phonological input. Phonological rules are involved in the process of phonological generation. The SPE style operations on the computational phonology have used the rewriting rule ordering or the finite state transducer (Bird 1995; Bird and Ellison 1994; Gildea and Jurafsky 1996; Kaplan and Kay 1994). Those approaches, however, should reduce complicated * This paper was supported by the Second Brain Korea 21. Copyright 2007 by Kim Dong-Sung and Chang-Hwa Roh 1 Anyone can visit the website of Lee et al. (2005) and generate standard pronunciation at http://urimal.cs.pusan.ac.kr. 241 orderings because of huge amount of rewriting rules and rule orderings among themselves (Gildea and Jurafsky 1996). Other differently motivated approaches have suggested the data- oriented models, using a pronunciation corpus to derive legitimate outputs (Daelemans, Gillis and Durieux 1994; Johnson 1984). In this study, we use the learning method of TBL that was proposed by Brill (1995). We design a set of templates and abstract transformations of possible pronunciations. For the experiments, we set up an aligned corpus between the text based on the Korean standard orthography and the text based on the Korean standard pronunciation. We conducted an experiment on generating the standard pronunciation with the TBL algorithm, using this corpus. We use the phonotactic constraints to reduce the complexity of TBL process. As …

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