3,513 research outputs found
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
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.Comment: 9 pages, 4 figures, accepted for EMNLP'17 Workshop on Computational
Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Understanding the flow of information in Deep Neural Networks (DNNs) is a
challenging problem that has gain increasing attention over the last few years.
While several methods have been proposed to explain network predictions, there
have been only a few attempts to compare them from a theoretical perspective.
What is more, no exhaustive empirical comparison has been performed in the
past. In this work, we analyze four gradient-based attribution methods and
formally prove conditions of equivalence and approximation between them. By
reformulating two of these methods, we construct a unified framework which
enables a direct comparison, as well as an easier implementation. Finally, we
propose a novel evaluation metric, called Sensitivity-n and test the
gradient-based attribution methods alongside with a simple perturbation-based
attribution method on several datasets in the domains of image and text
classification, using various network architectures.Comment: ICLR 201
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