RANCC: Rationalizing Neural Networks via Concept Clustering

Housam Khalifa Bashier, Mi-Young Kim, Randy Goebel


Abstract
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.
Anthology ID:
2020.coling-main.286
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3214–3224
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.286
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.286.pdf