| Nematus: a toolkit for neural machine translation R Sennrich, O Firat, K Cho, A Birch, B Haddow, J Hitschler, ... Proceedings of the EACL 2017 Software Demonstrations, 65–68, 2017 | 457 | 2017 |
| Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation S Läubli, R Sennrich, M Volk Proceedings of EMNLP 2018, 4791–4796, 2018 | 357 | 2018 |
| A Set of Recommendations for Assessing Human–Machine Parity in Language Translation S Läubli, S Castilho, G Neubig, R Sennrich, Q Shen, A Toral Journal of Artificial Intelligence Research 67, 653–672, 2020 | 150 | 2020 |
| Assessing Post-Editing Efficiency in a Realistic Translation Environment S Läubli, M Fishel, G Massey, M Ehrensberger-Dow, M Volk Proceedings of MT Summit XIV Workshop on Post-editing Technology and …, 2013 | 109 | 2013 |
| When Google Translate is better than some human colleagues, those people are no longer colleagues S Läubli, D Orrego-Carmona Proceedings of Translating and the Computer 39, 59–69, 2017 | 85 | 2017 |
| Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain S Läubli, C Amrhein, P Düggelin, B Gonzalez, A Zwahlen, M Volk Proceedings of MT Summit, 267–272, 2019 | 56 | 2019 |
| Translation technology research and human–computer interaction (HCI) S Läubli, S Green The Routledge Handbook of Translation and Technology, 2019 | 23 | 2019 |
| Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model C Amrhein, F Schottmann, R Sennrich, S Läubli Proceedings of ACL 2023, 4486–4506, 2023 | 22 | 2023 |
| What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT L Fischer, S Läubli Proceedings of EAMT 2020, 215–224, 2020 | 13 | 2020 |
| Statistical modelling and automatic tagging of human translation processes S Läubli, U Germann New directions in empirical translation process research: Exploring the …, 2016 | 12 | 2016 |
| The Impact of Text Presentation on Translator Performance S Läubli, P Simianer, J Wuebker, G Kovacs, R Sennrich, S Green Target: International Journal of Translation Studies 34 (2), 309–342, 2022 | 11 | 2022 |
| Combining Statistical Machine Translation and Translation Memories with Domain Adaptation S Läubli, M Fishel, M Volk, M Weibel Proceedings of the 19th Nordic Conference of Computational Linguistics …, 2013 | 9 | 2013 |
| Towards mapping of alpine route descriptions M Piotrowski, S Läubli, M Volk Proceedings of the 6th Workshop on Geographic Information Retrieval, 1-2, 2010 | 8 | 2010 |
| Statistical Machine Translation for Automobile Marketing Texts S Läubli, M Fishel, M Weibel, M Volk Proceedings of MT Summit XIV, 2013 | 5 | 2013 |
| Machine translation for professional translators S Läubli University of Zurich, 2020 | 3 | 2020 |
| mtrain: A Convenience Tool for Machine Translation S Läubli, M Müller, B Horat, M Volk Proceedings of EAMT 2018, 357, 2018 | 3 | 2018 |
| Automatic TM Cleaning through MT and POS Tagging: Autodesk's Submission to the NLP4TM 2016 Shared Task A Zwahlen, O Carnal, S Läubli arXiv preprint arXiv:1605.05906, 2016 | 3 | 2016 |
| Sentiment Analysis for Media Reputation Research S Läubli, M Schranz, U Christen, M Klenner Proceedings of KONVENS 2012 (PATHOS 2012 workshop), 274-281, 2012 | 3 | 2012 |
| A comparison of translation performance between DeepL and Supertext A Flückiger, C Amrhein, T Graf, P Schläpfer, F Schottmann, S Läubli arXiv preprint arXiv:2502.02577, 2025 | | 2025 |
| MT developers M Volk, S Läubli Handbook of the Language Industry: Contexts, Resources and Profiles 20, 101, 2024 | | 2024 |
Martin VolkProfessor of Computational Linguistics, University of ZurichПотвърден имейл адрес: cl.uzh.ch