Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Leila Arras; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek

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arXiv · 1706.07206

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Leila Arras·Grégoire Montavon·Klaus-Robert Müller·Wojciech Samek

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Abstract

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.

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Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek. 2017-06-22. Explaining Recurrent Neural Network Predictions in Sentiment Analysis. https://arxiv.org/abs/1706.07206

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