Corpus-level and Concept-based Explanations for Interpretable Document Classification

Using attention weights to identify information that is important for models' decision making is a popular approach to interpret attention-based neural networks, which is commonly realized via creating a heat-map for every single document based on attention weights. However, this interpretation method is fragile. In this paper, we propose a corpus-level explanation approach, which aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. Using this idea as the fundamental building block, we further propose a concept-based explanation method that can automatically learn higher-level concepts and their importance to model prediction task. Our concept-based explanation method is built upon a novel Abstraction-Aggregation Network, which can automatically cluster important keywords during an end-to-end training process. We apply these methods to the document classification task and show that they are powerful in extracting semantically meaningful keywords and concepts. Our consistency analysis results based on an attention-based Naive Bayes Classifier also demonstrate these keywords and concepts are important for model predictions.