Publication details [#6958]

Han, Chung-hye, Benoit Lavoie, Martha Palmer, Owen Rambow, Richard Kittredge, Tanya Korelsky, Nari Kim and Myunghee Kim. 2000. Handling structural divergences and recovering dropped arguments in a Korean /English machine translation system. In White, John S., ed. Envisioning Machine Translation in the information future (Lecture Notes in Artificial Intelligence 1934). Cham: Springer. pp. 40–53.
Publication type
Article in jnl/bk
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Source language
Target language


This paper describes an approach for handling structural divergences and recovering dropped arguments in an implemented Korean to English machine translation system. The approach relies on canonical predicate-argument structures (or dependency structures), which provide a suitable pivot representation for the handling of structural divergences and the recovery of dropped arguments. It can also be converted to and from the interface representations of many off-the-shelf parsers and generators.
Source : Bitra