Self-interpretable Convolutional Neural Networks for Text Classification

Understand

Deep learning models for natural language processing (NLP) are inherently complex and often viewed as black box in nature.

• This paper develops an approach for interpreting convolutional neural networks for text classification problems by exploiting the local-linear models inherent in ReLU-DNNs.
• The CNN model combines the word embedding through convolutional layers, filters them using max-pooling, and optimizes using a ReLU-DNN for classification.
• To get an overall self-interpretable model, the system of local linear models from the ReLU DNN are mapped back through the max-pool filter to the appropriate n-grams.