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@@ -3,19 +3,123 @@ 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](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 three models implemented in this repository are: -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/). +- `deepmind_deep_lstm` reproduces the experimental settings of the DeepMind paper for the LSTM reader +- `deepmind_attentive_reader` reproduces the experimental settings of the DeepMind paper for the Attentive reader +- `deep_bidir_lstm_2x128` implements a two-layer bidirectional LSTM reader + +## Our results + +We trained the three models during 2 to 4 days on a Titan Black GPU. The following results were obtained: + + +<table width="416" cellpadding="2" cellspacing="2"> +<tr> +<td valign="top" align="center"> </td> +<td colspan="2" valign="top" align="center">DeepMind </td> +<td colspan="2" valign="top" align="center">Us </td> +</tr> +<tr> +<td valign="top" align="center"> </td> +<td colspan="2" valign="top" align="center">CNN </td> +<td colspan="2" valign="top" align="center">CNN </td> +</tr> +<tr> +<td valign="top" align="center"> </td> +<td valign="top" align="center">Valid </td> +<td valign="top" align="center">Test </td> +<td valign="top" align="center">Valid </td> +<td valign="top" align="center">Test </td> +</tr> +<tr> +<td valign="top" align="center">Attentive Reader </td> +<td valign="top" align="center"><b>61.6</b> </td> +<td valign="top" align="center"><b>63.0</b> </td> +<td valign="top" align="center">59.37 </td> +<td valign="top" align="center">61.07 </td> +</tr> +<tr> +<td valign="top" align="center">Deep Bidir LSTM </td> +<td valign="top" align="center">- </td> +<td valign="top" align="center">- </td> +<td valign="top" align="center"><b>59.76</b> </td> +<td valign="top" align="center"><b>61.62</b> </td> +</tr> +<tr> +<td valign="top" align="center">Deep LSTM Reader</td> +<td valign="top" align="center">55.0</td> +<td valign="top" align="center">57.0</td> +<td valign="top" align="center">46</td> +<td valign="top" align="center">47</td> +</tr> +</table> + +Here is an example of attention weights used by the attentive reader model on an example: + +<img src="https://raw.githubusercontent.com/thomasmesnard/DeepMind-Teaching-Machines-to-Read-and-Comprehend/master/doc/attention_weights_example.png" width="816px" height="652px" /> + + +## Requirements + +Software dependencies: + +* [Theano](https://github.com/Theano/Theano) GPU computing library library +* [Blocks](https://github.com/mila-udem/blocks) deep learning framework +* [Fuel](https://github.com/mila-udem/fuel) data pipeline for Blocks + +Optional dependencies: + +* Blocks Extras and a Bokeh server for the plot + +We recommend using [Anaconda 2](https://www.continuum.io/downloads) and installing them with the following commands (where `pip` refers to the `pip` command from Anaconda): + + pip install git+git://github.com/Theano/Theano.git + pip install git+git://github.com/mila-udem/fuel.git + pip install git+git://github.com/mila-udem/blocks.git -r https://raw.githubusercontent.com/mila-udem/blocks/master/requirements.txt + +Anaconda also includes a Bokeh server, but you still need to install `blocks-extras` if you want to have the plot: + + pip install git+git://github.com/mila-udem/blocks-extras.git + +The corresponding dataset is provided by [DeepMind](https://github.com/deepmind/rc-data) but if the script does not work (or you are tired of waiting) you can check [this preprocessed version of the dataset](http://cs.nyu.edu/~kcho/DMQA/) by [Kyunghyun Cho](http://www.kyunghyuncho.me/). + + +## Running + +Set the environment variable `DATAPATH` to the folder containing the DeepMind QA dataset. The training questions are expected to be in `$DATAPATH/deepmind-qa/cnn/questions/training`. + +Run: + + cp deepmind-qa/* $DATAPATH/deepmind-qa/ + +This will copy our vocabulary list `vocab.txt`, which contains a subset of all the words appearing in the dataset. + +To train a model (see list of models at the beginning of this file), run: + + ./train.py model_name + +Be careful to set your `THEANO_FLAGS` correctly! For instance you might want to use `THEANO_FLAGS=device=gpu0` if you have a GPU (highly recommended!) + + +## Reference -Reference -========= [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 -======= +## Credits + [Thomas Mesnard](https://github.com/thomasmesnard) [Alex Auvolat](https://github.com/Alexis211) -[Étienne Simon](https://github.com/ejls)
\ No newline at end of file +[Étienne Simon](https://github.com/ejls) + + +## Acknowledgments + +We would like to thank the developers of Theano, Blocks and Fuel at MILA for their excellent work. + +We thank Simon Lacoste-Julien from SIERRA team at INRIA, for providing us access to two Titan Black GPUs. + + |