Housam Khalifa Bashier


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RANCC: Rationalizing Neural Networks via Concept Clustering
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 28th International Conference on Computational Linguistics

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.