  We present ProtoTEx, a novel white-box NLP classification architecture based
on prototype networks. ProtoTEx faithfully explains model decisions based on
prototype tensors that encode latent clusters of training examples. At
inference time, classification decisions are based on the distances between the
input text and the prototype tensors, explained via the training examples most
similar to the most influential prototypes. We also describe a novel
interleaved training algorithm that effectively handles classes characterized
by the absence of indicative features. On a propaganda detection task, ProtoTEx
accuracy matches BART-large and exceeds BERT-large with the added benefit of
providing faithful explanations. A user study also shows that prototype-based
explanations help non-experts to better recognize propaganda in online news.
