2,661 research outputs found
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently
used to explain the predictions of Deep Learning models, specifically in the
domain of text classification. Given different attribution-based explanations
to highlight relevant words for a predicted class label, experiments based on
word deleting perturbation is a common evaluation method. This word removal
approach, however, disregards any linguistic dependencies that may exist
between words or phrases in a sentence, which could semantically guide a
classifier to a particular prediction. In this paper, we present a
feature-based evaluation framework for comparing the two attribution methods on
customer reviews (public data sets) and Customer Due Diligence (CDD) extracted
reports (corporate data set). Instead of removing words based on the relevance
score, we investigate perturbations based on embedded features removal from
intermediate layers of Convolutional Neural Networks. Our experimental study is
carried out on embedded-word, embedded-document, and embedded-ngrams
explanations. Using the proposed framework, we provide a visualization tool to
assist analysts in reasoning toward the model's final prediction.Comment: NIPS 2018 Workshop on Challenges and Opportunities for AI in
Financial Services: the Impact of Fairness, Explainability, Accuracy, and
Privacy, Montr\'eal, Canad
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
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