Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling
This work addresses interpretability for users of sequential data models, but it is incremental as it builds on existing prototype-based methods with new constraints.
The paper tackles the disparity between prototype-based explanations and original inputs in sequential data modeling by proposing a Self-Explaining Selective Model (SESM) that uses prototypical parts for interpretability, achieving competitive accuracy in experiments.
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.