Code accompanying the paper Gated Attention Reader for Text Comprehension.
- Python 2.7
- Theano and all dependencies (latest)
- Lasagne (latest)
- Numpy
- Maybe more, just use
pip installif you get an error
You can get the preprocessed data files from here. Extract the tar files to the data/ directory. Ensure that the symbolic links point to folders with training/, validation/ and test/ directories for each dataset.
You can also get the pretrained Glove vectors from the above link. Place this file in the data/ directory as well.
Issue the command:
python run.py --dataset <wdw|cnn|dailymail|cbtcn|cbtne>
Complete list of options:
$ python run.py --help
Using gpu device 0: GeForce GTX TITAN X (CNMeM is disabled, cuDNN 5105)
usage: run.py [-h] [--mode MODE] [--nlayers NLAYERS] [--dataset DATASET]
[--seed SEED] [--gating_fn GATING_FN]
optional arguments:
-h, --help show this help message and exit
--mode MODE run mode - (0-train+test, 1-train only, 2-test only,
3-val only) (default: 0)
--nlayers NLAYERS Number of reader layers (default: 3)
--dataset DATASET Dataset - (cnn || dailymail || cbtcn || cbtne || wdw)
(default: wdw)
--seed SEED Seed for different experiments with same settings
(default: 1)
--gating_fn GATING_FN
Gating function (T.mul || Tsum || Tconcat) (default:
T.mul)
To set dataset specific hyperparameters modify config.py.
Make sure to add THEANO_FLAGS=device=cpu,floatX=float32 before any command if you are running on a CPU.
If you use this code please cite the following:
Dhingra, B., Liu, H., Yang, Z., Cohen, W. W., & Salakhutdinov, R. (2016). Gated-Attention Readers for Text Comprehension. arXiv preprint arXiv:1606.01549.
@article{dhingra2016gated,
title={Gated-Attention Readers for Text Comprehension},
author={Dhingra, Bhuwan and Liu, Hanxiao and Yang, Zhilin, and Cohen, William W and Salakhutdinov, Ruslan},
journal={arXiv preprint arXiv:1606.01549},
year={2016}
}
Report bugs and missing info to bdhingraATandrewDOTcmuDOTedu (replace AT, DOT appropriately).
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent