  Recent years have witnessed increasing interests 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 and text classification requires
hierarchical modelling to aggregate local information in order to deal with
topic and label shifts more effectively. As such, interpretation by words or
phrases only cannot faithfully explain model decisions in text classification.
This paper 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.
