26.10.2016 - 10:15
Post-editing has been shown to be more efficient than translation from scratch in various languages and domains. However, efficiency gains are often offset by a suboptimal interplay between translation workbenches (frontend) and machine translation systems (backend), meaning that translators cannot take advantage – or are not even aware – of what state-of-the-art machine translation technology can offer.
In this talk, I will summarise lessons learned from implementing post-editing workflows in the automotive and software localisation industry, as well as compare and contrast them with findings from academic research. I will focus on recent work on interactive machine translation protocols, in particular, pointing out open research problems and opportunities in the intersection of machine translation, human–computer interaction and translation process research.
Samuel Läubli holds a Master’s degree in Artificial Intelligence from the University of Edinburgh. From 2014 to 2016, he designed and deployed machine translation systems as a Senior Computational Linguist at Autodesk. The systems were primarily used for software localisation through post-editing. In August 2016, Samuel started a Ph.D. at the University of Zurich, focussing on interactive machine translation with neural networks.
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