Explaining Predictions of Non-Linear Classifiers in NLP

Understand

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.