Web: http://arxiv.org/abs/2106.13876

Sept. 19, 2022, 1:15 a.m. | Bodhisattwa Prasad Majumder, Oana-Maria Camburu, Thomas Lukasiewicz, Julian McAuley

cs.CL updates on arXiv.org arxiv.org

Models that generate extractive rationales (i.e., subsets of features) or
natural language explanations (NLEs) for their predictions are important for
explainable AI. While an extractive rationale provides a quick view of the
features most responsible for a prediction, an NLE allows for a comprehensive
description of the decision-making process behind a prediction. However,
current models that generate the best extractive rationales or NLEs often fall
behind the state-of-the-art (SOTA) in terms of task performance. In this work,
we bridge this …

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