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Arda Tezcan and Bram Bulté
Previous research has shown that simple methods of augmenting machine translation training data and input sentences with translations of similar sentences (or fuzzy matches), retrieved from a translation memory or bilingual corpus, lead to considerable i...
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Arda Tezcan, Bram Bulté and Bram Vanroy
We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value o...
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Rebecca Webster, Margot Fonteyne, Arda Tezcan, Lieve Macken and Joke Daems
Due to the growing success of neural machine translation (NMT), many have started to question its applicability within the field of literary translation. In order to grasp the possibilities of NMT, we studied the output of the neural machine system of Go...
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Lieve Macken, Daniel Prou and Arda Tezcan
This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish ...
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Vincent Vandeghinste, Tom Vanallemeersch, Liesbeth Augustinus, Bram Bulté, Frank Van Eynde, Joris Pelemans, Lyan Verwimp, Patrick Wambacq, Geert Heyman, Marie-Francine Moens, Iulianna van der Lek-Ciudin, Frieda Steurs, Ayla Rigouts Terryn, Els Lefever, Arda Tezcan, Lieve Macken, Véronique Hoste, Joke Daems, Joost Buysschaert, Sven Coppers, Jan Van den Bergh and Kris LuytenaddShow full author listremoveHide full author list
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these...
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