Verb Pattern Based Korean-Chinese Machine Translation System

Verb Pattern Based Korean-Chinese Machine Translation System Kim, Changhyun NLP Team Human Information Processing Dept. ETRI,Korea chkim@etri.re.kr Hong, Munpyo NLP Team Human Information Processing Dept. ETRI,Korea Hmp63 1 08 @etri.re.icr Yang,Sung NLP Team Human Information Processing Dept. ETRI,Korea siyang@etri.re.kr Kim, Young Kil NLP Team Human Information Processing Dept. ETRI,Korea kimyk@etri.re.kr Seo, Young Ae NLP Team Human Information Processing Dept. ETRI,Korea yaseo@etri.re.kr Sung-Kwon Choi NLP Team Human Information Processing Dept. ETRI,Korea choisk@etri.re.kr Abstract This paper describes our ongoing Korean-Chinese machine translation system, which is based on verb patterns. A verb pattern consists of a source language pattern part for analysis and a target language pattern part for generation. Knowledge description on lexical level makes it easy to achieve accurate analyses and natural, correct generation. These features are very important and effective in machine translation between languages with quite different linguistic structures including Korean and Chinese. We performed a preliminary evaluation of our current system and reported the result in the paper. 1� Introduction Machine translation requires correct analysis of source languages, appropriate generation into target languages and a large amount of knowledge such as rules, statistics or patterns. Especially persistent and consistent knowledge acquisition and management, monotonic improvement of performance according to knowledge accumulation are the keys to machine translation. Rule based methods suffer from knowledge acquisition and consistent management. Statistical methods show no connections between the previous statistical knowledge and the new statistical knowledge and have difficulty in reflecting linguistic phenomena and peculiarities directly into knowledge. Patterns have several formats such as sentence-based patterns(Kaji Hiroyuki(1992)), phrase-based patterns(Watanabe Hideo(1993)) and collocation-based pafterns(Smadja(1996), Kevin McTait(1999)). Sentence-based patterns uses a whole sentence as a pattern and transfer the input sentence in one time. It suffers mainly from data sparseness. Phrase-based patterns can be used for both syntactic analysis and transfer. The transfer is done phrase by phrase. Collocation-based patterns are used for lexical transfer, that is, the transfer unit is a word. ETRI performed a verb-pattern based Korean-English machine translation from 1999 to 2001 and experienced strong points on the side of knowledge acquisition, consistent management of linguistic peculiarities between two languages and monotonic increase in system performance according to the number of patterns(Kim, Y.K. et al.(2001); Seo,Y.A. et al(2000)). A verb pattern consists of a source language pattern for analysis and a target language pattern for generation. Knowledge description on lexical level makes it easy to achieve accurate analyses and natural, correct generations. So, accurate 157 `‘) Complex� •...4VP Structure Patterns Structure Analyzer Case Structure Analyzer Dependency Tree Generator VP Structure Patte Verb Patterns Case Constraint Patte Dependency Rules ADVP Patterns Chunker Morphological 4_1 Normalizer a ormalization Rules MRD Tagged Corpus Knowledge Building Tool Bilingual Corpus Law Corpus Frozen Expression Processor a Verb Phrase Linker Relative Clause Linker --to VP Generator a �� NP Generator Translation Memory Link Patterns Relative Clause Linking Rules VP Patterns NP Patterns Post Processor Morphological Analyzer PreProcessor Unified Lexicon 4--F-T— nalyzed Dictionary ../ 1 Pre-Processing Rubs Morphological Generator Generation Codes Post Processing Rule..s.1—• Post Processor Chines Output …

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