Learning translation templates for closely related languages
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
2773 PART 1
756 - 762
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27511
Many researchers have worked on example-based machine translation and different techniques have been investigated in the area. In literature, a method of using translation templates learned from bilingual example pairs was proposed. The paper investigates the possibility of applying the same idea for close languages where word order is preserved. In addition to applying the original algorithm for example pairs, we believe that the similarities between the translated sentences may always be learned as atomic translations. Since the word order is almost always preserved, there is no need to have any previous knowledge to identify the corresponding differences. The paper concludes that applying this method for close languages may improve the performance of the system.
- Conference Paper 2294
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Daybelge, T.; Cicekli I. (2011)Example-Based Machine Translation (EBMT) is a corpus based approach to Machine Translation (MT), that utilizes the translation by analogy concept. In our EBMT system, translation templates are extracted automatically from ...
Öz, Z.; Cicekli I. (Springer Verlag, 1998)TTL (Translation Template Learner) algorithm learns lexical level correspondences between two translation examples by using analogical reasoning. The sentences used as translation examples have similar and different parts ...
Cicekli I.; Güvenir H.A. (2001)A mechanism for learning lexical correspondences between two languages from sets of translated sentence pairs is presented. These lexical level correspondences are learned using analogical reasoning between two translation ...