This is code for the project : https://arxiv.org/abs/1902.10186 . We will be updating it in coming weeks to include instructions on how to download and process the data and run the experiments.
This project requires compiling pytorch from source master branch or use pytorch-nightly. We use features that are not in stable release. It also requires installation of torchtext version 0.4.0 from source.
After installation of above, please use pip install -r requirements.txt.
Also, python -m spacy download en to include the english language pack for spacy if not already present.
We are providing code to run experiments on all datasets . For obtaining ADR tweets data, please contact us directly (a large portion of tweets we have used in this experiments have been removed from twitter website).
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Clone the repository as
git clone https://github.com/successar/AttentionExplanation.git Transparency(Note this is important.) -
Set your PYTHONPATH to include the directory path which contains this repository (All imports in the code are of form Transparency.* -- If you see error
ModuleNotFoundError: No module named 'Transparency', most probably your PYTHONPATH is not set.).
For example if your cloned repository reside in /home/username/Transparency, then one way to do this is export PYTHONPATH="/home/username" from command line or add it to your ~/.bashrc .
- Go to the
Transparency/preprocessfolder and follow the instructions to process datasets.
- From the main folder, run
python train_and_run_experiments_bc.py --dataset {dataset_name} --data_dir . --output_dir outputs/ --attention {attention_type} --encoder {encoder_type}
Valid values for dataset_name are [sst, imdb, 20News_sports, tweet, Anemia, Diabetes, AgNews].
Valid values for encoder_type is [cnn, lstm, average].
Valid values for attention_type is [tanh, dot].
For example, if you want to run experiments for IMDB dataset with CNN encoder and Tanh attention, please use python train_and_run_experiments_bc.py --dataset imdb --data_dir . --output_dir outputs/ --attention tanh --encoder cnn
- From the main folder, run
python train_and_run_experiments_qa.py --dataset {dataset_name} --data_dir . --output_dir outputs/ --attention {attention_type} --encoder {encoder_type}
Valid values for dataset_name are [snli, cnn, babi_1, babi_2, babi_3].
Valid values for encoder_type is [cnn, lstm, average].
Valid values for attention_type is [tanh, dot].
For example, if you want to run experiments for snli dataset with LSTM encoder and Tanh attention, please use python train_and_run_experiments_bc.py --dataset snli --data_dir . --output_dir outputs/ --attention tanh --encoder lstm
Both BC and QA tasks will generate the graphs used in paper in the folder Transparency/graph_outputs .
You can also browse our graphs here -- https://successar.github.io/AttentionExplanation/docs/ .