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Generating explanations for neural networks has become crucial for theirapplications in real-world with respect to reliability and trustworthiness. Innatural language processing, existing methods usually provide importantfeatures which are words or phrases selected from an input text as anexplanation, but ignore the interactions between them. It poses challenges forhumans to interpret an explanation and connect it to model prediction. In thiswork, we build hierarchical explanations by detecting feature interactions.Such explanations visualize how words and phrases are combined at differentlevels of the hierarchy, which can help users understand the decision-making ofblack-box models. The proposed method is evaluated with three neural textclassifiers (LSTM, CNN, and BERT) on two benchmark datasets, via both automaticand human evaluations. Experiments show the effectiveness of the proposedmethod in providing explanations that are both faithful to models andinterpretable to humans.

Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection