e2e-ie-release
Code accompanying End-to-End Information Extraction without Token-Level Supervision
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Python
End-to-End Information Extraction without Token-Level Supervision
Code for the paper End-to-End Information Extraction without Token-Level Supervision
The results presented in the paper can be found in emnlp-results
Installing dependencies
pip install -r requirements.txt
Training and evaluating the multi-output pointer net models
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python train.py <atis|movie|restaurant>will train a model. The best model (evaluated on the val set) will be saved as best.npz in the work dir -
python test.py <atis|movie|restaurant>will load best.npz and evaluate the model on the test set
Training and evaluating the Bi-LSTM baseline
- Set the dataset in
baselines/bilstm/runner.py -
python baselines/bilstm/runner.pytrains a model, evaluate on val set, and for every improvement evaluate on test set.
Boostrap sampling
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python evaluate.py actual1.json actual2.json expected.jsonwill run bootstrap sampling to estimate p-values for actual1.json vs. actual2.json