Deep learning based on neural networks is claimed to be a technology that will finally bring real intelligence to machine translation and will break the quality barrier for which machine translation has been criticized for decades.
In this webinar, Maxim Khalilov demonstrates the potential of deep learning as it applies to machine translation (MT) by describing the combined MT-deep learning system in simple terms and contrasting it with state-of-the-art statistical MT systems. He also shares some real-life examples on how exactly deep learning is breaking the quality barrier.
Maxim Khalilov is the Machine Translation CTO at a German language service provider, Matrix Communications GmbH. He has a PhD in statistical machine translation (Polytechnic University of Catalonia, 2009) and is the author of more than 30 scientific publications.
Prior to joining Matrix, he worked as a Team Lead at an MT startup, bmmt, as an R&D manager at TAUS, and as a post-doctoral researcher at the University of Amsterdam. Maxim is an MBA candidate at IE Business School.