Question Answering Corpus
This repository contains a script to generate question/answer pairs using
CNN and Daily Mail articles downloaded from the Wayback Machine.
For a detailed description of this corpus please read:
Teaching Machines to Read and Comprehend, Hermann et al., NIPS 2015.
Please cite the paper if you use this corpus in your work.
Bibtex
@inproceedings{nips15_hermann,
author = {Karl Moritz Hermann and Tom\'a\v{s} Ko\v{c}isk\'y and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
title = {Teaching Machines to Read and Comprehend},
url = {http://arxiv.org/abs/1506.03340},
booktitle = "Advances in Neural Information Processing Systems (NIPS)",
year = "2015",
}
Download Processed Version
In case the script does not work you can also download the processed data sets from [http://cs.nyu.edu/~kcho/DMQA/]. This should help in situations where the underlying data is not accessible (Wayback Machine partially down).
Running the Script
Prerequisites
Python 2.7, wget, libxml2, libxslt, python-dev and virtualenv. libxml2 must be version 2.9.1.
You can install libxslt from here: http://xmlsoft.org/libxslt/downloads.html
sudo pip install virtualenv
sudo apt-get install python-dev
Download Script
mkdir rc-data
cd rc-data
wget https://github.com/deepmind/rc-data/raw/master/generate_questions.py
Download and Extract Metadata
wget https://storage.googleapis.com/deepmind-data/20150824/data.tar.gz -O -, tar -xz --strip-components=1
The news article metadata is ~1 GB.
Enter Virtual Environment and Install Packages
virtualenv venv
source venv/bin/activate
wget https://github.com/deepmind/rc-data/raw/master/requirements.txt
pip install -r requirements.txt
You may need to install libxml2 development packages to install lxml:
sudo apt-get install libxml2-dev libxslt-dev
Download URLs
python generate_questions.py --corpus=[cnn/dailymail] --mode=download
This will download news articles from the Wayback Machine. Some URLs may be
unavailable. The script can be run again and will cache
URLs that already have been downloaded. Generation of questions can run
without all URLs downloaded successfully.
Generate Questions
python generate_questions.py --corpus=[cnn/dailymail] --mode=generate
Note, this will generate ~1,000,000 small files for the Daily Mail so an SSD is
preferred.
Questions are stored in [cnn/dailymail]/questions/ in the following format:
[URL]
[Context]
[Question]
[Answer]
[Entity mapping]
Deactivate Virtual Environment
deactivate
Verifying Test Sets
wget https://github.com/deepmind/rc-data/raw/master/expected_[cnn/dailymail]_test.txt
comm -3 <(cat expected_[cnn/dailymail]_test.txt) <(ls [cnn/dailymail]/questions/test/)
The filenames of the questions in the first column are missing generated questions. No output means everything is downloaded and generated correctly.