68 citations · 100 across the 4 of their papers we have counts for
6 papers
What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT
Lukas Fischer, Samuel Läubli
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 err…
A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
Samuel Läubli, Sheila Castilho, Graham Neubig +3
The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a nu…
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 +3
Neural machine translation (NMT) has set new quality standards in automatic translation, yet its effect on post-editing productivity is still pending thorough investigation. We emp…
Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation
Samuel Läubli, Rico Sennrich, Martin Volk
Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese--English news translation task. We empirically test…
Nematus: a Toolkit for Neural Machine Translation
Rico Sennrich, Orhan Firat, Kyunghyun Cho +8
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-pe…
Automatic TM Cleaning through MT and POS Tagging: Autodesk's Submission to the NLP4TM 2016 Shared Task
Alena Zwahlen, Olivier Carnal, Samuel Läubli
We describe a machine learning based method to identify incorrect entries in translation memories. It extends previous work by Barbu (2015) through incorporating recall-based machi…