Samuel Läubli


What’s the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT
Lukas Fischer | Samuel Läubli
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

Machine translation (MT) has been shown to produce a number of errors that require human post-editing, but the extent to which professional human translation (HT) contains such errors has not yet been compared to MT. We compile pre-translated documents in which MT and HT are interleaved, and ask professional translators to flag errors and post-edit these documents in a blind evaluation. We find that the post-editing effort for MT segments is only higher in two out of three language pairs, and that the number of segments with wrong terminology, omissions, and typographical problems is similar in HT.


Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain
Samuel Läubli | Chantal Amrhein | Patrick Düggelin | Beatriz Gonzalez | Alena Zwahlen | Martin Volk
Proceedings of Machine Translation Summit XVII: Research Track


Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation
Samuel Läubli | Rico Sennrich | Martin Volk
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.


Nematus: a Toolkit for Neural Machine Translation
Rico Sennrich | Orhan Firat | Kyunghyun Cho | Alexandra Birch | Barry Haddow | Julian Hitschler | Marcin Junczys-Dowmunt | Samuel Läubli | Antonio Valerio Miceli Barone | Jozef Mokry | Maria Nădejde
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.


Combining Statistical Machine Translation and Translation Memories with Domain Adaptation
Samuel Läubli | Mark Fishel | Martin Volk | Manuela Weibel
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)

Statistical Machine Translation for Automobile Marketing Texts
Samuel Läubli | Mark Fishel | Manuela Weibel | Martin Volk
Proceedings of Machine Translation Summit XIV: Posters

Assessing post-editing efficiency in a realistic translation environment
Samuel Läubli | Mark Fishel | Gary Massey | Maureen Ehrensberger-Dow | Martin Volk
Proceedings of the 2nd Workshop on Post-editing Technology and Practice