Ondřej Bojar is an Assistant Professor at Charles University, Institute of Formal and Applied Linguistics (UFAL). Since his participation at the JHU summer engineering workshop in 2006 where the MT system Moses was released, Ondřej Bojar has been primarily active in the field of machine translation (MT), regularly taking part and later also co-organizing the WMT evaluation campaigns and contributing to the best practices of MT evaluation. Ondřej Bojar is the main author of the hybrid system Chimera which outperformed all competing systems in 2013 through 2015 (including Google Translate) in English-to-Czech translation. A variant of that system has been used in several commercial contracts of the department. Ondřej Bojar is now catching up with neural MT (NMT) and his main interest (aside from reaching again the best translation performance) lies in the study of the representations learned by the deep learning models. Is NMT learning any representations of sentence *meaning*, or is it merely a much advanced and softer variant of the copy-paste translation as performed by the previous approaches? His talk takes place on Tuesday, January 16, 2018 at 13:00 in room E105.

Neural Machine Translation: From Basics to Semiotics

In my talk, I will highlight the benefit that neural machine translation (NMT) has over previous statistical approaches to MT. I will then present the current state of the art in neural machine translation, briefly describing the current best architectures and their performance and limitations. In the second part of the talk, I will outline my planned search for correspondence between sentence meaning as traditionally studied by linguistics (or even semantics and semiotics) and the continuous representations learned by neural networks.