ArisToCAT. Assessing the comprehensibility of automatic translations

Begin - Einde 
2017 - 2022 (lopend)
Vakgroep(en) 
Vakgroep Vertalen, Tolken en Communicatie

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Abstract

Machine translation systems cannot guarantee that the text they produce will be fluent and coherent in both syntax and semantics. Erroneous words and syntax occur frequently in machine-translated text, leaving the reader to guess parts of the intended message.

This project (i) analyzes eye movement data to investigate to what extent the lack of predictability in texts that were created by MT impairs comprehension, and (ii) tries to automatically estimate the comprehensibility of machine-translated text.

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