| 2026 | EAMT | Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution. | Jania Hackenbuchner, Jasper Degraeuwe, Arda Tezcan, Joke Daems |
| 2024 | EAMT | Automatic detection of (potential) factors in the source text leading to gender bias in machine translation. | Jania Hackenbuchner, Arda Tezcan, Joke Daems |
| 2023 | EAMT | Developing User-centred Approaches to Technological Innovation in Literary Translation (DUAL-T). | Paola Ruffo, Joke Daems, Lieve Macken |
| 2022 | EAMT | DeBiasByUs: Raising Awareness and Creating a Database of MT Bias. | Joke Daems, Jania Hackenbuchner |
| 2022 | EAMT | Writing in a second Language with Machine translation (WiLMa). | Margot Fonteyne, Maribel Montero Perez, Joke Daems, Lieve Macken |
| 2022 | LREC | GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation. | Toon Colman, Margot Fonteyne, Joke Daems, Nicolas Dirix, Lieve Macken |
| 2020 | EAMT | Assessing the Comprehensibility of Automatic Translations (ArisToCAT). | Lieve Macken, Margot Fonteyne, Arda Tezcan, Joke Daems |
| 2014 | LREC | On the origin of errors: A fine-grained analysis of MT and PE errors and their relationship. | Joke Daems, Lieve Macken, Sonia Vandepitte |