2 code implementations • LREC 2022 • Bram Vanroy, Lieve Macken
Because we also collected translation process data in the form of keystroke logging, our dataset can be used as part of different research strands such as translation process research, learner corpus research, and corpus-based translation studies.
no code implementations • EAMT 2022 • Lieve Macken, Bram Vanroy, Luca Desmet, Arda Tezcan
This study focuses on English-Dutch literary translations that were created in a professional environment using an MT-enhanced workflow consisting of a three-stage process of automatic translation followed by post-editing and (mainly) monolingual revision.
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 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 • 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.
no code implementations • WS 2015 • V, Vincent eghinste, Tom Vanallemeersch, Frank Van Eynde, Geert Heyman, Sien Moens, Joris Pelemans, Patrick Wambacq, Iulianna Van der Lek - Ciudin, Arda Tezcan, Lieve Macken, V{\'e}ronique Hoste, Eva Geurts, Mieke Haesen
no code implementations • LREC 2014 • Joke Daems, Lieve Macken, V, Sonia epitte
In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not enough to know that the output of one system is better than the output of another system.
no code implementations • LREC 2012 • Lieve Macken, Veronique Hoste, Mari{\"e}lle Leijten, Luuk Van Waes
In this paper we report on an extension to the keystroke logging program Inputlog in which we aggregate the logged process data from the keystroke (character) level to the word level.