lngvietthang / DeepMind-Teaching-Machines-to-Read-and-Comprehend

Implementation of "Teaching Machines to Read and Comprehend" proposed by Google DeepMind

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DeepMind : Teaching Machines to Read and Comprehend

This repository contains an implementation of the two models (the Deep LSTM and the Attentive Reader) described in Teaching Machines to Read and Comprehend by Karl Moritz Hermann and al., NIPS, 2015. This repository also contains an implementation of a Deep Bidirectional LSTM.

Models are implemented using Theano and Blocks. Datasets are implemented using Fuel.

The corresponding dataset is provided by DeepMind but if the script does not work you can check http://cs.nyu.edu/~kcho/DMQA/ by Kyunghyun Cho.

Reference

Teaching Machines to Read and Comprehend, by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman and Phil Blunsom, Neural Information Processing Systems, 2015.

Credits

Thomas Mesnard

Alex Auvolat

Étienne Simon

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Implementation of "Teaching Machines to Read and Comprehend" proposed by Google DeepMind

License:MIT License


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Language:Python 100.0%