Deep Natural Language Understanding

author: Kyunghyun Cho, Courant Institute of Mathematical Sciences, New York University (NYU)
published: Aug. 23, 2016,   recorded: August 2016,   views: 20412


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In this lecture, I start with a claim that natural language understanding can largely be approached as building a better language model and explain three widely-adopted approaches to language modelling. They are n-gram language modelling, feedforward neural language modelling and recurrent language modelling. As I develop from the traditional n-gram language model toward recurrent language model, I discuss the concepts of data sparsity and generalization via continuous space representations. I then continue on to the recent development of a novel paradigm in machine translation based on recurrent language modelling, often called neural machine translation. The lecture concludes with three new opportunities in natural language processing/understanding made possible by the introduction of continuous space representations in deep neural networks.

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Reviews and comments:

Comment1 Krishna, December 12, 2016 at 10:35 a.m.:

Very nice...

Comment2 sarah, May 20, 2017 at 3:33 a.m.:

Thank you for your presentation! I am studying translation and interpreting but unfortunately, I have not been very good at understanding all the principles that you have written. I hope there might be a way that I can understand them. If any of you know how to help me with it, it could be very helpful for me to give you some input from my own standpoint of view as a translator/interpreter (; Good luck on your work!

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