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ArrasL README
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code code release Jun 29, 2017
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run_example.ipynb code release Jun 29, 2017

README.md

Description

This code release contains an implementation of two relevance decomposition methods, Layer-wise Relevance Propagation (LRP) and Sensitivity Analysis (SA), for a bidirectional LSTM, as described in the paper Explaining Recurrent Neural Network Predictions in Sentiment Analysis by L. Arras, G. Montavon, K.-R. Müller and W. Samek, 2017

Note that our implementation is generic and can be easily extended to unidirectional LSTMs, or to other applications than NLP.

Dependencies

Python>=3.5 + Numpy + Matplotlib, or alternatively simply install Anaconda.

Using Anaconda you can e.g. create a Python 3.6 environment: conda create -n py36 python=3.6 anaconda

Then activate it with: source activate py36

Before being able to use the code, you might need to run in the terminal: export PYTHONPATH=$PYTHONPATH:$pwd

Usage

The folder model/ contains a word-based bidirectional LSTM model, that was trained for five-class sentiment prediction of phrases and sentences on the Stanford Sentiment Treebank dataset, as released by the authors in Visualizing and Understanding Neural Models in NLP by J. Li, X. Chen, E. Hovy and D. Jurafsky, 2016

The folder data/ contains the test set sentences of the Stanford Sentiment Treebank, preprocessed by lowercasing, as was done in Visualizing and Understanding Neural Models in NLP by J. Li, X. Chen, E. Hovy and D. Jurafsky, 2016

The notebook run_example.ipynb provides a usage example of the code, its performs LRP and SA on a test sentence.

Acknowledgments

Visualizing and Understanding Neural Models in NLP by J. Li, X. Chen, E. Hovy and D. Jurafsky, 2016 code

Visualizing and Understanding Neural Models in NLP by J. Li, X. Chen, E. Hovy and D. Jurafsky, 2016 paper

Long Short Term Memory Units, repo by W. Zaremba

Stanford Sentiment Treebank, dataset by R. Socher et al., 2013

Citation

@INPROCEEDINGS{arras2017,
    title     = {Explaining Recurrent Neural Network Predictions in Sentiment Analysis},
    author    = {Leila Arras and Gr{\'e}goire Montavon and Klaus-Robert M{\"u}ller and Wojciech Samek},
    booktitle = {Proceedings of the EMNLP 2017 Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
    year      = {2017},
    pages     = {159-168},
    publisher = {Association for Computational Linguistics},
    url       = {http://aclweb.org/anthology/W/W17/W17-5221.pdf}
}

More information

For further research and projects involving LRP, visit heatmapping.org