| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1579 | 2023 |
| Beam search strategies for neural machine translation M Freitag, Y Al-Onaizan arXiv preprint arXiv:1702.01806, 2017 | 481 | 2017 |
| Experts, errors, and context: A large-scale study of human evaluation for machine translation M Freitag, G Foster, D Grangier, V Ratnakar, Q Tan, W Macherey Transactions of the Association for Computational Linguistics 9, 1460-1474, 2021 | 364 | 2021 |
| Language models are multilingual chain-of-thought reasoners F Shi, M Suzgun, M Freitag, X Wang, S Srivats, S Vosoughi, HW Chung, ... arXiv preprint arXiv:2210.03057, 2022 | 265 | 2022 |
| Fast domain adaptation for neural machine translation M Freitag, Y Al-Onaizan arXiv preprint arXiv:1612.06897, 2016 | 206 | 2016 |
| Results of WMT22 metrics shared task: Stop using BLEU–neural metrics are better and more robust M Freitag, R Rei, N Mathur, C Lo, C Stewart, E Avramidis, T Kocmi, ... Proceedings of the Seventh Conference on Machine Translation (WMT), 46-68, 2022 | 205 | 2022 |
| Findings of the 2021 conference on machine translation (WMT21) F Akhbardeh, A Arkhangorodsky, M Biesialska, O Bojar, R Chatterjee, ... Proceedings of the sixth conference on machine translation, 1-88, 2021 | 196 | 2021 |
| Results of the WMT21 metrics shared task: Evaluating metrics with expert-based human evaluations on TED and news domain M Freitag, R Rei, N Mathur, C Lo, C Stewart, G Foster, A Lavie, O Bojar Proceedings of the Sixth Conference on Machine Translation, 733-774, 2021 | 172 | 2021 |
| Results of the WMT20 metrics shared task N Mathur, J Wei, M Freitag, Q Ma, O Bojar Proceedings of the Fifth Conference on Machine Translation, 688-725, 2020 | 156 | 2020 |
| Prompting palm for translation: Assessing strategies and performance D Vilar, M Freitag, C Cherry, J Luo, V Ratnakar, G Foster arXiv preprint arXiv:2211.09102, 2022 | 154 | 2022 |
| BLEU might be guilty but references are not innocent M Freitag, D Grangier, I Caswell arXiv preprint arXiv:2004.06063, 2020 | 146 | 2020 |
| Ensemble distillation for neural machine translation M Freitag, Y Al-Onaizan, B Sankaran arXiv preprint arXiv:1702.01802, 2017 | 114 | 2017 |
| Results of the WMT15 metrics shared task M Stanojević, A Kamran, P Koehn, O Bojar Proceedings of the Tenth Workshop on Statistical Machine Translation, 256-273, 2015 | 112 | 2015 |
| Modeling punctuation prediction as machine translation S Peitz, M Freitag, A Mauser, H Ney Proceedings of the 8th International Workshop on Spoken Language Translation …, 2011 | 99 | 2011 |
| Scaling laws for neural machine translation B Ghorbani, O Firat, M Freitag, A Bapna, M Krikun, X Garcia, C Chelba, ... arXiv preprint arXiv:2109.07740, 2021 | 97 | 2021 |
| Findings of the 2023 conference on machine translation (WMT23): LLMs are here but not quite there yet T Kocmi, E Avramidis, R Bawden, O Bojar, A Dvorkovich, C Federmann, ... WMT23-Eighth Conference on Machine Translation, 198--216, 2023 | 94 | 2023 |
| INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback W Xu, D Wang, L Pan, Z Song, M Freitag, WY Wang, L Li arXiv preprint arXiv:2305.14282, 2023 | 86 | 2023 |
| High quality rather than high model probability: Minimum Bayes risk decoding with neural metrics M Freitag, D Grangier, Q Tan, B Liang Transactions of the Association for Computational Linguistics 10, 811-825, 2022 | 79* | 2022 |
| APE at scale and its implications on MT evaluation biases M Freitag, I Caswell, S Roy arXiv preprint arXiv:1904.04790, 2019 | 75 | 2019 |
| The devil is in the errors: Leveraging large language models for fine-grained machine translation evaluation P Fernandes, D Deutsch, M Finkelstein, P Riley, AFT Martins, G Neubig, ... arXiv preprint arXiv:2308.07286, 2023 | 62 | 2023 |
Hermann NeyRWTH Aachen UniversityVerified email at cs.rwth-aachen.de
