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, from free-form explanations to deductive explanations, to argumentative explanations (with the richest structure). Focusing on the automated fact verification task, we provide illustrations of the use and usefulness of our formalization for evaluating explanations, tailored to their varying structures.

翻译：随着自然语言处理中深度神经模型日益复杂且变得不透明，对其可解释性的需求愈发迫切。为预测提供简洁连贯的合理化解释已成为一个新兴研究热点。本文作为立场论文，倡导建立包含关键概念与特性的形式化框架以支持对合理化解释的系统性评估。我们勾勒出这样一种形式化框架——专门适配结构日益复杂的各类合理化解释，从自由形式解释、演绎解释到论辩解释（结构最为丰富）。聚焦自动化事实验证任务，我们通过实例说明该形式化方法如何针对不同结构解释类型实现评估功能并彰显其实用价值。