  Many text classification applications require models with satisfying
performance as well as good interpretability. Traditional machine learning
methods are easy to interpret but have low accuracies. The development of deep
learning models boosts the performance significantly. However, deep learning
models are typically hard to interpret. In this work, we propose interpretable
capsule networks (iCapsNets) to bridge this gap. iCapsNets use capsules to
model semantic meanings and explore novel methods to increase interpretability.
The design of iCapsNets is consistent with human intuition and enables it to
produce human-understandable interpretation results. Notably, iCapsNets can be
interpreted both locally and globally. In terms of local interpretability,
iCapsNets offer a simple yet effective method to explain the predictions for
each data sample. On the other hand, iCapsNets explore a novel way to explain
the model's general behavior, achieving global interpretability. Experimental
studies show that our iCapsNets yield meaningful local and global
interpretation results, without suffering from significant performance loss
compared to non-interpretable methods.
