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 • 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 • Arda Tezcan
This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.
1 code implementation • Informatics 2021 • Arda Tezcan, Bram Bulté, Bram Vanroy
We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT).
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 • ACL 2019 • Bram Bulte, Arda Tezcan
We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM).
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