  Prototype learning, a popular machine learning method designed for inherently
interpretable decisions, leverages similarities to learned prototypes for
classifying new data. While it is mainly applied in computer vision, in this
work, we build upon prior research and further explore the extension of
prototypical networks to natural language processing. We introduce a learned
weighted similarity measure that enhances the similarity computation by
focusing on informative dimensions of pre-trained sentence embeddings.
Additionally, we propose a post-hoc explainability mechanism that extracts
prediction-relevant words from both the prototype and input sentences. Finally,
we empirically demonstrate that our proposed method not only improves
predictive performance on the AG News and RT Polarity datasets over a previous
prototype-based approach, but also improves the faithfulness of explanations
compared to rationale-based recurrent convolutions.
