Learning translation templates for closely related languages
Author
Altıntaş, Kemal
Güvenir, H. Altay
Date
2003Source Title
Knowledge-Based Intelligent Information and Engineering Systems
Print ISSN
0302-9743
Publisher
Springer, Berlin, Heidelberg
Volume
2773
Pages
756 - 762
Language
English
Type
Conference PaperItem Usage Stats
193
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views
157
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Abstract
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.
Keywords
Example-Based Machine Translation (EBMT)Language Processing
Machine Translation
Operational Systems
Example Based Machine Translations
Original Algorithms
Translation Templates
Word Orders
Algorithms
Computer Science
Linguistics
Neural Networks
Semantics
Computational Linguistics
Knowledge Based Systems
Computer Programming Languages
Translation (languages)
Permalink
http://hdl.handle.net/11693/27511Published Version (Please cite this version)
https://doi.org/10.1007/978-3-540-45224-9_102https://doi.org/10.1007/978-3-540-45224-9
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