Towards a Framework for Evaluating Explanations in Automated Fact Verification

arXiv:2403.20322v1 Announce Type: new
Abstract: As deep neural models in NLP become more complex, and as a consequence opaque, the necessity to interpret them becomes greater. A burgeoning interest has emerged in rationalizing explanations to provide short and coherent justifications for predictions. In this position paper, we advocate for a formal framework for key concepts and properties about rationalizing explanations to support their evaluation systematically. We also outline one such formal framework, tailored to rationalizing explanations of increasingly complex structures, …

abstract arxiv automated become cs.cl framework nlp paper predictions them type verification