Skip to content

Latest commit

 

History

History
92 lines (72 loc) · 3.52 KB

README.md

File metadata and controls

92 lines (72 loc) · 3.52 KB
Raw

multiPRover

PyTorch code for our NAACL 2021 paper:

multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning

Swarnadeep Saha, Prateek Yadav, and Mohit Bansal

Installation

This repository is tested on Python 3.8.3.
You should install multiPRover on a virtual environment. All dependencies can be installed as follows:

pip install -r requirements.txt

Download Dataset

Download the dataset as follows:

bash scripts/download_data.sh

Training Iterative-multiPRover

Iterative-multiPRover can be trained by running the following script:

bash scripts/train_iterative_mprover.sh

This will train on the depth-5 dataset. The trained model folder will be saved inside output folder.

Testing Iterative-multiPRover

The trained Iterative-multiPRover model can be tested by running the following script:

bash scripts/test_iterative_mprover.sh

This will output the QA accuracy, save the node predictions at prediction_nodes_dev.lst and the predicted edge logits at prediction_edge_logits_dev.lst.

Running ILP Inference

Once the node predictions and the edge logits are saved, you can run ILP inference to get edge predictions as follows:

bash scripts/run_inference_mprover.sh

This will save the edge predictions inside the model folder.

Evaluation

Once QA, node and edge predictions are saved, you can compute all metrics (QA accuracy, Node F1, Edge F1, Proof F1 and Full accuracy) as follows:

bash scripts/get_results_mprover.sh

Zero-shot Evaluation on Birds-Electricity

Run the above testing, inference and evaluation scripts to test the depth-5 trained Iterative-mPRover model on the Birds-Electricity dataset by appropriately changing the data-dir path to data/birds-electricity in all the scripts and lines 197 and 198 in utils.py with test.jsonl and meta-test.jsonl.

Training Multilabel-mPRover

Run the following scripts (similar steps as before but changing the paths wherever applicable):

bash scripts/train_multilabel_mprover.sh
bash scripts/test_multilabel_mprover.sh
bash scripts/run_inference_mprover.sh
bash scripts/get_results_mprover.sh

Trained Models

We also release our trained multiPRover models on depth-5 dataset here. These contain the respective QA, node and edge predictions and you can reproduce the exact validation set results from the paper by running the evaluation script.

Running Other Ablations

Ablation models from the paper can be run by uncommenting parts of the code (like choosing a particular depth). Please refer to the comments in utils_iterative_mprover.py for details.

Citation

@inproceedings{saha2021multiprover,
  title={multi{PR}over: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning},
  author={Saha, Swarnadeep and Yadav, Prateek and Bansal, Mohit},
  booktitle={NAACL},
  year={2021}
}

Related Citation

multiPRover builds on top of our previous work, PRover. Feel free to check it out and cite, if you find the paper or the code helpful.

@inproceedings{saha2020prover,
  title={{PR}over: Proof Generation for Interpretable Reasoning over Rules},
  author={Saha, Swarnadeep and Ghosh, Sayan and Srivastava, Shashank and Bansal, Mohit},
  booktitle={EMNLP},
  year={2020}
}