Browsing by Subject "Machine translations"
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Item Open Access An English-to-Turkish interlingual MT system(Springer, 1998-10) Hakkani, Dilek Zeynep; Tür, Gökhan; Oflazer, Kemal; Mitamura, T.; Nyberg, E.H.This paper describes the integration of a Turkish generation system with the KANT knowledge-based machine translation system to produce a prototype English-Turkish interlingua-based machine translation system. These two independently constructed systems were successfully integrated within a period of two months, through development of a module which maps KANT interlingua expressions to Turkish syntactic structures. The combined system is able to translate completely and correctly 44 of 52 benchmark sentences in the domain of broadcast news captions. This study is the first known application of knowledge-based machine translation from English to Turkish, and our initial results show promise for future development. © Springer-Verlag Berlin Heidelberg 1998.Item Open Access A ranking method for example based machine translation results by learning from user feedback(Springer New York LLC, 2011-10) Daybelge, T.; Cicekli, I.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 bilingual aligned corpora by substituting the similarities and differences in pairs of translation examples with variables. In the earlier versions of the discussed system, the translation results were solely ranked using confidence factors of the translation templates. In this study, we introduce an improved ranking mechanism that dynamically learns from user feedback. When a user, such as a professional human translator, submits his evaluation of the generated translation results, the system learns "context-dependent co-occurrence rules" from this feedback. The newly learned rules are later consulted, while ranking the results of the subsequent translations. Through successive translation-evaluation cycles, we expect that the output of the ranking mechanism complies better with user expectations, listing the more preferred results in higher ranks. We also present the evaluation of our ranking method which uses the precision values at top results and the BLEU metric. © 2010 Springer Science+Business Media, LLC.