Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a "word deleting" perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.
@article{arxiv.1606.07298,
title = {Explaining Predictions of Non-Linear Classifiers in NLP},
author = {Leila Arras and Franziska Horn and Grégoire Montavon and Klaus-Robert Müller and Wojciech Samek},
journal= {arXiv preprint arXiv:1606.07298},
year = {2016}
}
Comments
7 pages, 3 figures, Paper accepted for 1st Workshop on Representation Learning for NLP at ACL 2016