HQ-LV / LRP_for_LSTM

Layer-wise Relevance Propagation (LRP) for LSTMs

Description

This repository provides a reference implementation of Layer-wise Relevance Propagation (LRP) for an LSTM, as initially proposed in the paper Explaining Recurrent Neural Network Predictions in Sentiment Analysis, L. Arras, G. Montavon, K.-R. Müller and W. Samek WASSA@EMNLP'2017 [arXiv:1706.07206].

Additionally, it includes an implementation of Sensitivity Analysis (SA) and GradientxInput (GI), i.e. of gradient-based relevances.

Note that our implementation is generic and can be easily extended to unidirectional LSTMs, or to other application domains than Natural Language Processing (NLP).

A few hints on how to adapt the code to your needs can be found here.

Dependencies

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

Using conda, 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 (SST) dataset, as released by the authors in Visualizing and Understanding Neural Models in NLP, J. Li, X. Chen, E. Hovy and D. Jurafsky, 2016

The folder data/ contains the test set sentences of the Stanford Sentiment Treebank (SST), preprocessed by lowercasing, as was done in Visualizing and Understanding Neural Models in NLP, 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, SA and GI on a test sentence.
(To correctly render the notebook on GitHub you can copy the notebook's URL to nbviewer)

Follow-up work

Here are some follow-up works using LRP on various recurrent neural network models (non-exhaustive list):

• Evaluating Neural Network Explanation Methods using Hybrid Documents and Morphosyntactic Agreement, N. Poerner, H. Schütze and B. Roth ACL 2018

• Explaining Therapy Predictions with Layer-Wise Relevance Propagation in Neural Networks, Y. Yang, V. Tresp, M. Wunderle and P.A. Fasching IEEE ICHI 2018 [preprint]

• Analyzing Neuroimaging Data Through Recurrent Deep Learning Models, A.W. Thomas, H.R. Heekeren, K.-R. Müller and W. Samek arXiv:1810.09945

• Evaluating Recurrent Neural Network Explanations, L. Arras, A. Osman, K.-R. Müller and W. Samek BlackboxNLP@ACL 2019 [oral presentation slides] [arXiv:1904.11829]

Acknowledgments

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

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

Long Short Term Memory Units, repo by W. Zaremba

Stanford Sentiment Treebank (SST), 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},
    doi       = {10.18653/v1/W17-5221},
    url       = {https://www.aclweb.org/anthology/W17-5221}
}

More information

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