Neural machine translation -- Progress Report

Abstract:

Neural machine translation is a recently proposed framework for machine translation, which is purely based on neural networks. Neural machine translation radically departs from the existing, widely-used, often phrase-based statistical machine translation by viewing the task of machine translation as a supervised, structured output prediction problem and solving it with recurrent neural networks. In this talk, I will describe in detail what neural machine translation is and discuss recent advances which have made it possible for neural machine translation system to be competitive with the conventional statistical approach. I will conclude the talk with a big question: is natural language special?

Bio:

Kyunghyun Cho is an assistant professor in the Department of Computer Science, Courant Institute of Mathematical Sciences and the Center for Data Science at New York University (NYU) (starting September, 2015). Previously, he was a postdoctoral researcher at the University of Montreal under the supervision of Prof. Yoshua Bengio after obtaining a doctorate degree at Aalto University (Finland) in early 2014. Kyunghyun's main research interests include neural networks, generative models and their applications, especially, to language understanding.