Assessing the Comprehensibility of Automatic Translations (ArisToCAT)

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.

Writing in a second Language with Machine translation (WiLMa)

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.

Machine Translation Translation

GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation

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.

Machine Translation Translation

Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level

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.

Machine Translation NMT +1