Corpus-level and Concept-based Explanations for Interpretable Document Classification
This addresses the problem of unreliable interpretability methods for attention-based neural networks in NLP, offering more robust explanations for researchers and practitioners, though it is incremental as it builds on existing attention mechanisms.
The paper tackles the fragility of single-document attention heatmaps for interpretability in document classification by proposing corpus-level and concept-based explanation methods, which extract semantically meaningful keywords and concepts and are shown to be important for model predictions via consistency analysis.
Using attention weights to identify information that is important for models' decision-making is a popular approach to interpret attention-based neural networks. This is commonly realized in practice through the generation of a heat-map for every single document based on attention weights. However, this interpretation method is fragile, and easy to find contradictory examples. In this paper, we propose a corpus-level explanation approach, which aims to capture causal relationships between keywords and model predictions via learning the importance of keywords for predicted labels across a training corpus based on attention weights. Based on this idea, we further propose a concept-based explanation method that can automatically learn higher-level concepts and their importance to model prediction tasks. 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 Naïve Bayes classifier also demonstrate these keywords and concepts are important for model predictions.