no code implementations • LREC 2022 • Toon Colman, Margot Fonteyne, Joke Daems, Nicolas Dirix, Lieve Macken
In the present paper, we describe a large corpus of eye movement data, collected during natural reading of a human translation and a machine translation of a full novel.
no code implementations • EAMT 2020 • Lieve Macken, Margot Fonteyne, Arda Tezcan, Joke Daems
The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output.
no code implementations • EAMT 2022 • Margot Fonteyne, Maribel Montero Perez, Joke Daems, Lieve Macken
The WiLMa project aims to assess the effects of using machine translation (MT) tools on the writing processes of second language (L2) learners of varying proficiency.
no code implementations • LREC 2020 • Margot Fonteyne, Arda Tezcan, Lieve Macken
Several studies (covering many language pairs and translation tasks) have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems.