Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection
arXiv:2004.02015
Abstract
Generating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness. In natural language processing, existing methods usually provide important features which are words or phrases selected from an input text as an explanation, but ignore the interactions between them. It poses challenges for humans to interpret an explanation and connect it to model prediction. In this work, we build hierarchical explanations by detecting feature interactions. Such explanations visualize how words and phrases are combined at different levels of the hierarchy, which can help users understand the decision-making of black-box models. The proposed method is evaluated with three neural text classifiers (LSTM, CNN, and BERT) on two benchmark datasets, via both automatic and human evaluations. Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models and interpretable to humans.
ACL 2020
References in corpus (11)
- Distributed Representations of Words and Phrases and their Compositionality
- Axiomatic Attribution for Deep Networks
- Learning Important Features Through Propagating Activation Differences
- Enhanced LSTM for Natural Language Inference
- Consistent Individualized Feature Attribution for Tree Ensembles
- Understanding Neural Networks through Representation Erasure
- Towards Robust Interpretability with Self-Explaining Neural Networks
- Model Agnostic Supervised Local Explanations
- Interpretation of Prediction Models Using the Input Gradient
- Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
- LS-Tree: Model Interpretation When the Data Are Linguistic