  Many NLP applications require models to be interpretable. However, many
successful neural architectures, including transformers, still lack effective
interpretation methods. A possible solution could rely on building explanations
from domain knowledge, which is often available as plain, natural language
text. We thus propose an extension to transformer models that makes use of
external memories to store natural language explanations and use them to
explain classification outputs. We conduct an experimental evaluation on two
domains, legal text analysis and argument mining, to show that our approach can
produce relevant explanations while retaining or even improving classification
performance.
