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Dr. Samuel Läubli

Dr. Samuel Läubli

Dr. Samuel Läubli

ZHAW Angewandte Linguistik
Institut für Übersetzen und Dolmetschen

samuel.laeubli@zhaw.ch

Persönliches Profil

Tätigkeit an der ZHAW als

Operativer Leiter, Forschungs- und Arbeitsbereich Mensch-Maschine-Kommunikation

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Maschinelle Übersetzung
Computerlinguistik
Mensch-Maschine-Interaktion

Aus- und Fortbildung

2020: PhD, Computerlinguistik (Maschinelle Übersetzung), Universität Zürich
2014: MSc, Artificial Intelligence, University of Edinburgh
2012: BA, Computerlinguistik und Sprachtechnologie (Hauptfach), Informatik (Nebenfach), Universität Zürich

Beruflicher Werdegang

seit 2016: Chief Technology Officer, TextShuttle (www.textshuttle.ai)
seit 2016: Lehrbeauftragter, Institut für Computerlinguistik, Universität Zürich (www.cl.uzh.ch)
2014–2016: Senior Computational Linguist, Autodesk (www.autodesk.com)

Publikationen

Publikationen vor Tätigkeit an der ZHAW

Selection:

Läubli, Samuel, Sheila Castilho, Graham Neubig, Rico Sennrich, Qinlan Shen, and Antonio Toral. 2020. A Set of Recommendations for Assessing Human–Machine Parity in Language Translation. Journal of Artificial Intelligence Research 67:653–672. Full text available at doi.org/10.1613/jair.1.11371

Läubli, Samuel, Rico Sennrich, and Martin Volk. 2018. Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation. In Proceedings of EMNLP, pages 4791–4796, Brussels, Belgium. Full text and presentation (recording) available at doi.org/10.18653/v1/D18-1512

Sennrich, Rico, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, and Maria Nadejde. 2017. Nematus: a Toolkit for Neural Machine Translation. In Proceedings of EACL, pages 65–68, Valencia, Spain. Full text available at aclanthology.org/E17-3017, code available at github.com/EdinburghNLP/nematus

Full List:

Google Scholar, scholar.google.com/citations
ORCID, orcid.org/0000-0001-5362-4106

Weitere Beiträge

Invited Talks (Selection):

22.01.2021. Has machine translation really reached parity with professional human translation? The impact of document-level context on quality evaluation and translator performance. Invited Seminar at European Commission, Directorate-General for Translation. Online.

09.12.2020. The Impact of Text Presentation on Human MT Evaluation and Translator Productivity. Invited Talk at Lilt. Online.

10.11.2020. Human–Machine Parity in Language Translation? A set of recommendations for evaluating strong MT systems. Invited Talk at Google Research. Online.

04.12.2018. Neural Machine Translation and Post-Editing. Invited seminar at the German Translation Unit, Directorate-General for Translation, European Parliament. Luxembourg.

05.12.2017. 3 Reasons Why Neural Machine Translation is a Breakthrough. Invited keynote at SlatorCon Zurich. Zurich, Switzerland. Summary available at tinyurl.com/yrv5vm69, video interview available at vimeo.com/247766051

Media Coverage (Selection):

"What’s New in Machine Translation with TextShuttle CTO Samuel Läubli". Slator, 28.05.2021: tinyurl.com/24x6mpzn

"Klar kann ein maschinelles Übersetzungssystem Lyrik übersetzen". Übersetzerhaus Looren, 24.02.2021: tinyurl.com/m72vzc7e

"(More) Advanced Human-Computer Interaction for Translators: A Conversation with Samuel Läubli and Nico Herbig". The ATA Chronicle 50/1, 2021: tinyurl.com/j8c6vvfk

"New Research Flips the Script on CAT Tools — Literally". Slator, 25.11.2020: tinyurl.com/ku86bh3b

"MT Post-Editing Boosts Swiss Bank’s Translation Productivity by Up to 60%, Study Finds". Slator, 14.09.2019: tinyurl.com/ykk5zx4m

"Human translators are still on top—for now". MIT Technology Review, 05.09.2018: tinyurl.com/3s5tt3yf

"Swiss Science Foundation Grants USD 0.5m to Take Neural MT Beyond the Sentence". Slator, 23.06.2017: tinyurl.com/4ampcsde