Phrase-pattern-based korean to english machine translation using two level translation pattern selection

Abstract

Pattern-Based Machine Translation is one of the machine translation methods which performs syntactic analysis and structure transfer at the same time using bilingual patterns. PBMT is used to expand the length of patterns up to sentence-length in order to reduce ambiguities in translation, but it brought out the problem of rapidly increased patterns. We propose a model which shortens the length of patterns to phrase-length and reduces ambiguities in translation by using two-level translation pattern selection method. In the first level, the proper translation patterns are selected by using a hybrid method of exact example matching and semantic constraint by thesaurus. In the second level, the most natural translation pattern for the verb phrase is selected among the selected translation pattern categories by using statistic information of the target language. By using this proposed model, we could shorten the length of patterns without raising the ambiguities in translation.