| Reducing gender bias in neural machine translation as a domain adaptation problem D Saunders, B Byrne arXiv preprint arXiv:2004.04498, 2020 | 154 | 2020 |
| Domain adaptation and multi-domain adaptation for neural machine translation: A survey D Saunders Journal of Artificial Intelligence Research 75, 351-424, 2022 | 106 | 2022 |
| Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It D Saunders, R Sallis, B Byrne arXiv preprint arXiv:2010.05332, 2020 | 84 | 2020 |
| The practical ethics of bias reduction in machine translation: Why domain adaptation is better than data debiasing M Tomalin, B Byrne, S Concannon, D Saunders, S Ullmann Ethics and Information Technology, 1-15, 2021 | 69 | 2021 |
| An operation sequence model for explainable neural machine translation F Stahlberg, D Saunders, B Byrne arXiv preprint arXiv:1808.09688, 2018 | 31 | 2018 |
| Domain adaptive inference for neural machine translation D Saunders, F Stahlberg, A de Gispert, B Byrne arXiv preprint arXiv:1906.00408, 2019 | 29 | 2019 |
| Multi-representation ensembles and delayed SGD updates improve syntax-based NMT D Saunders, F Stahlberg, A De Gispert, B Byrne arXiv preprint arXiv:1805.00456, 2018 | 29 | 2018 |
| Using context in neural machine translation training objectives D Saunders, F Stahlberg, B Byrne arXiv preprint arXiv:2005.01483, 2020 | 26 | 2020 |
| First the worst: Finding better gender translations during beam search D Saunders, R Sallis, B Byrne arXiv preprint arXiv:2104.07429, 2021 | 25 | 2021 |
| Domain adaptation for neural machine translation D Saunders | 25 | 2021 |
| SGNMT--A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies F Stahlberg, E Hasler, D Saunders, B Byrne arXiv preprint arXiv:1707.06885, 2017 | 24 | 2017 |
| Cued@ wmt19: ewc&lms F Stahlberg, D Saunders, A de Gispert, B Byrne arXiv preprint arXiv:1906.05447, 2019 | 18* | 2019 |
| UCAM biomedical translation at WMT19: Transfer learning multi-domain ensembles D Saunders, F Stahlberg, B Byrne arXiv preprint arXiv:1906.05786, 2019 | 15 | 2019 |
| Addressing exposure bias with document minimum risk training: Cambridge at the WMT20 biomedical translation task D Saunders, B Byrne arXiv preprint arXiv:2010.05333, 2020 | 13 | 2020 |
| Why not be versatile? Applications of the SGNMT decoder for machine translation F Stahlberg, D Saunders, G Iglesias, B Byrne arXiv preprint arXiv:1803.07204, 2018 | 13 | 2018 |
| Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation D Saunders, K Olsen arXiv preprint arXiv:2306.04573, 2023 | 7 | 2023 |
| Inference-only sub-character decomposition improves translation of unseen logographic characters D Saunders, W Feely, B Byrne arXiv preprint arXiv:2011.06523, 2020 | 6 | 2020 |
| Morph-to-word transduction for accurate and efficient automatic speech recognition and keyword search A Ragni, D Saunders, P Zahemszky, J Vasilakes, MJF Gales, KM Knill 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 3 | 2017 |
| Domain adapted machine translation: What does catastrophic forgetting forget and why? D Saunders, S DeNeefe arXiv preprint arXiv:2412.17537, 2024 | | 2024 |
Bill ByrneProfessor of Information Engineering, Cambridge UniversityPreverjeni e-poštni naslov na eng.cam.ac.uk