ArisToCAT. Assessing the comprehensibility of automatic translations

Start - End 
2017 - 2020 (ongoing)
Department(s) 
Department of Translation, Interpreting and Communication

Tabgroup

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.

People

Supervisor(s)

Co-supervisor(s)

Phd Student(s)