Hierarchical Interpretation of Neural Text Classification

Recent years have witnessed increasing interest in developing interpretable models in Natural Language Processing (NLP). Most existing models aim at identifying input features such as words or phrases important for model predictions. Neural models developed in NLP, however, often compose word semantics in a hierarchical manner. As such, interpretation by words or phrases only cannot faithfully explain model decisions in text classification. This article proposes a novel Hierarchical Interpretable Neural Text classifier, called HINT, which can automatically generate explanations of model predictions in the form of label-associated topics in a hierarchical manner. Model interpretation is no longer at the word level, but built on topics as the basic semantic unit. Experimental results on both review datasets and news datasets show that our proposed approach achieves text classification results on par with existing state-of-the-art text classifiers, and generates interpretations more faithful to model predictions and better understood by humans than other interpretable neural text classifiers.(1)

Automated summary

In recent years, there has been increasing interest in developing interpretable models in Natural Language Processing (NLP). However, it is difficult to accurately explain model decisions by words or phrases when neural models in NLP compose word semantics hierarchically. This article proposes a novel Hierarchical Interpretable Neural Text classifier, called HINT, which generates explanations of model predictions in the form of label-associated topics. Experimental results show that HINT achieves comparable text classification results and provides better interpretations than other interpretable neural text classifiers.