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Deep Learning in Natural Language Processing
Ronan Collobert · Jason E Weston

Mon Dec 07 03:30 PM -- 05:30 PM (PST) @ Regency E/F

This tutorial will describe recent advances in deep learning techniques for Natural Language Processing (NLP). Traditional NLP approaches favour shallow systems, possibly cascaded, with adequate hand-crafted features. In constrast, we are interested in end-to-end architectures: these systems include several feature layers, with increasing abstraction at each layer. Compared to shallow systems, these feature layers are learnt for the task of interest, and do not require any engineering. We will show how neural networks are naturally well suited for end-to-end learning in NLP tasks. We will study multi-tasking different tasks, new semi-supervised learning techniques adapted to these deep architectures, and review end-to-end structured output learning. Finally, we will highlight how some of these advances can be applied to other fields of research, like computer vision, as well.

Author Information

Ronan Collobert (Apple)

Ronan Collobert received his master degree in pure mathematics from University of Rennes (France) in 2000. He then performed graduate studies in University of Montreal and IDIAP (Switzerland) under the Bengio brothers, and received his PhD in 2004 from University of Paris VI. He joined NEC Labs (USA) in January 2005 as a postdoc, and became a research staff member after about one year. His research interests always focused on large-scale machine-learning algorithms, with a particular interest in semi-supervised learning and deep learning architectures. Two years ago, his research shifted in the natural language processing area, slowly going towards automatic text understanding.

Jason E Weston (Meta AI)

Jason Weston received a PhD. (2000) from Royal Holloway, University of London under the supervision of Vladimir Vapnik. From 2000 to 2002, he was a researcher at Biowulf technologies, New York, applying machine learning to bioinformatics. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2004 to June 2009 he was a research staff member at NEC Labs America, Princeton. From July 2009 onwards he has been a research scientist at Google, New York. Jason Weston's current research focuses on various aspects of statistical machine learning and its applications, particularly in text and images.

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