DeepMind : Teaching Machines to Read and Comprehend
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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](https://github.com/Theano/Theano) and [Blocks](https://github.com/mila-udem/blocks). Datasets are implemented using [Fuel](https://github.com/mila-udem/fuel).

The corresponding dataset is provided by [DeepMind](https://github.com/deepmind/rc-data) but if the script does not work you can check [http://cs.nyu.edu/~kcho/DMQA/](http://cs.nyu.edu/~kcho/DMQA/) by [Kyunghyun Cho](http://www.kyunghyuncho.me/).

Reference
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[Teaching Machines to Read and Comprehend](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman and Phil Blunsom, Neural Information Processing Systems, 2015.


Credits
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[Thomas Mesnard](https://github.com/thomasmesnard)

[Alex Auvolat](https://github.com/Alexis211)

[Étienne Simon](https://github.com/ejls)