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Jun, 2021
基于知识的自我合理化:通过抽取和自然语言解释
Rationale-Inspired Natural Language Explanations with Commonsense
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Bodhisattwa Prasad Majumder, Oana-Maria Camburu, Thomas Lukasiewicz, Julian McAuley
TL;DR
介绍了一种自我合理化的框架RExC,旨在提供两种互补类型的解释(NLE和提取合理),并将其与背景知识结合起来,从而达到了任务最新水平的性能,并大幅度领先于现有模型,同时进行干扰分析表明,解释与预测之间存在高度的关联性。
Abstract
Explainable machine learning models primarily justify predicted labels using either
extractive rationales
(i.e., subsets of input features) or free-text
natural language explanations
(NLEs) as abstractive justifi
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