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README.md

Deep k-Nearest Neighbors and Interpretable NLP

This is the official code for the 2018 EMNLP Interpretability Workshop paper, Interpreting Neural Networks with Nearest Neighbors.

This repository contains the code for:

  • Deep k-Nearest Neighbors for text classification models. Allows pretrained word vectors, character level models, etc. on a number of datasets
  • Saliency map techniques for NLP, such as leave one out and gradient. Also includes our conformity leave one out method.
  • Create visualizations like the ones on our paper's supplementary website.
  • Temperature scaling as described in On Calibration of Modern Neural Networks
  • SNLI interpretations

Dependencies

This code is written in python using the highly underrated Chainer framework. If you know PyTorch, you will love it =).

Dependencies include:

If you want to do efficient nearest neighbor lookup:

  • Scikit-Learn (for KDTree)
  • nearpy (for locally sensitive hashing)

If you want to visualize saliency maps:

  • matplotlib

This code is built off Chainers text classification example. See their documentation and code to understand the basic layout of our project.

Files

To train a model:

python train_text_classifier.py --dataset stsa.binary --model cnn

The output directory result contains:

  • best_model.npz: a model snapshot, which won the best accuracy for validation data during training
  • vocab.json: model's vocabulary dictionary as a json file
  • args.json: model's setup as a json file, which also contains paths of the model and vocabulary
  • calib.json: The indices of the held out training data that will be used to calibrate the DkNN model

To run a model with and without DkNN:

python run_dknn.py --model-setup results/DATASET_MODEL/args.json
  • Where results/DATASET_MODEL/args.json is the argument log that is generated after training a model
  • This command will store the activations for all of the training data into a KDTree, calibrate the credibility values, and run the model with and without DkNN.

Word Vectors

In our paper, we used GloVe word vectors, though any pretrained vectors should work fine (word2vec, fastText, etc.). To obtain GloVe vectors, run the following commands.

wget http://nlp.stanford.edu/data/glove.840B.300d.zip
unzip glove.840B.300d.zip
rm glove.840B.300d.zip

Then pass the pretrained vectors in using the argument --word_vectors glove.840B.300d.txt when training a model using train_text_classifier.py

Temperature Scaling

scaling.py contains the temperature scaling implementation.

Interpretations and Visualizations

All of the code for generating interpretations using leave one out (conformity, confidence, or calibrated confidence) and first-order gradient is contained in interpretations.py. See the code for details on running with the desired settings. You should first train a model (see above), and then pass that in.

The code for visualization is also present in interpretations.py.

References

Please consider citing 1 if you found this code or our work beneficial to your research.

Interpreting Neural Networks with Nearest Neighbors

[1] Eric Wallace, Shi Feng, and Jordan Boyd-Graber, Interpreting Neural Networks with Nearest Neighbors.

@article{Wallace2018Neighbors,
  title={Interpreting Neural Networks with Nearest Neighbors},
  author={Eric Wallace and Shi Feng and Jordan Boyd-Graber},
  journal={arXiv preprint arXiv:1809.02847},  
  year={2018},  
}

Contact

For issues with code or suggested improvements, feel free to open a pull request.

To contact the authors, reach out to Eric Wallace (ewallac2@umd.edu) and Shi Feng (shifeng@cs.umd.edu).

About

Code for the 2018 EMNLP Interpretability Workshop Paper "Interpreting Neural Networks with Nearest Neighbors"

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