RANCC: Rationalizing Neural Networks via Concept Clustering
Housam Khalifa Bashier, Miyoung Kim, Randy G. Goebel · 2020
We propose a new self-explainable model for Natural Language Processing (NLP) text classification tasks.Our approach constructs explanations concurrently with the formulation of classification predictions.To do so, we extract a rationale from the text, then use it to predict a concept of interest as the final prediction.We provide three types of explanations: 1) rationale extraction, 2) a measure of feature importance, and 3) clustering of concepts.In addition, we show how our model can be compressed without applying complicated compression techniques.We experimentally demonstrate our explainability approach on a number of well-known text classification datasets.