Youngkil Kim


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Automatic Evaluation of English-to-Korean and Korean-to-English Neural Machine Translation Systems by Linguistic Test Points
Sung-Kwon Choi | Gyu-Hyeun Choi | Youngkil Kim
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation


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From To K/E: a Korean-English machine translation system based on idiom recognition and fail softening
Byong-Rae Ryu | Youngkil Kim | Sanghwa Yuh | Sangkyu Park
Proceedings of Machine Translation Summit VII

In this paper we describe and experimentally evaluate FromTo K/E, a rule-based Korean-English machine translation system adapting transfer methodology. In accordance with the view that a successful Korean-English machine translation system presumes a highly efficient robust Korean parser, we develop a parser reinforced with "Fail Softening", i.e. the long sentence segmentation and the recovery of failed parse trees. To overcome the language-typological differences between Korean and English, we adopt a powerful module for processing Korean multi-word lexemes and Korean idiomatic expressions. Prior to parsing Korean sentences, furthermore, we try to resolve the ambiguity of words with unknown grammatical functions on the basis of the collocation and subcategorization information. The results of the experimental evaluation show that the degree of understandability for sample 2000 sentences amounts to 2.67, indicating that the meaning of the translated English sentences is almost clear to users, but the sentences still include minor grammatical or stylistic errors up to max. 30% of the whole words.