  Prototype-based interpretability methods provide intuitive explanations of
model prediction by comparing samples to a reference set of memorized exemplars
or typical representatives in terms of similarity. In the field of sequential
data modeling, similarity calculations of prototypes are usually based on
encoded representation vectors. However, due to highly recursive functions,
there is usually a non-negligible disparity between the prototype-based
explanations and the original input. In this work, we propose a Self-Explaining
Selective Model (SESM) that uses a linear combination of prototypical concepts
to explain its own predictions. The model employs the idea of case-based
reasoning by selecting sub-sequences of the input that mostly activate
different concepts as prototypical parts, which users can compare to
sub-sequences selected from different example inputs to understand model
decisions. For better interpretability, we design multiple constraints
including diversity, stability, and locality as training objectives. Extensive
experiments in different domains demonstrate that our method exhibits promising
interpretability and competitive accuracy.
