摘要
Convolutional neural networks are one of the most important and widely used constructs in natural language processing and AI in general.In many applications,they have achieved state-of-the-art performance,with training time faster than the other alternatives.However,due to their limited interpretability,they are less favored by practitioners over attention-based models,like RNNs and self-attention(Transformers),which can be visualized and interpreted more intuitively by analyzing the attention-weight heat-maps.In this work,we present a visualization technique that can be used to understand the inner workings of text-based CNN models.We also show how this method can be used to generate adversarial examples and learn the shortcomings of the training data.
