用问答方式解释虚假信息判断依据，让模型推理过程可懂。
An Explainable Framework for Misinformation Identification via Critical Question Answering
- 基于论证模式与质疑问题理论构建可解释框架
- 构建首个包含3566个论据实例的中文语料库NLAS-CQ
- 通过回答关键问题提供人类可理解的推理依据
当前自然语言虚假信息检测多依赖序列分类方法，导致系统决策过程不透明。尽管自动事实核查已有可解释尝试，但自动化理性核查仍缺乏类似机制。本文提出一种基于论证模式与关键质疑问题理论的新可解释框架，用于事实性与理性虚假信息检测。为此，我们构建并发布了NLAS-CQ——首个融合3,566个教材式自然语言论证实例及4,687个对应关键问题回答的语料库。基于该语料库，我们实现并验证了融合分类与问答的分析框架，能识别论点中的虚假信息，并以关键问题形式向用户输出可理解的解释。
原文摘要 · Abstract (English)
Natural language misinformation detection approaches have been, to date, largely dependent on sequence classification methods, producing opaque systems in which the reasons behind classification as misinformation are unclear. While an effort has been made in the area of automated fact-checking to propose explainable approaches to the problem, this is not the case for automated reason-checking systems. In this paper, we propose a new explainable framework for both factual and rational misinformation detection based on the theory of Argumentation Schemes and Critical Questions. For that purpose, we create and release NLAS-CQ, the first corpus combining 3,566 textbook-like natural language argumentation scheme instances and 4,687 corresponding answers to critical questions related to these arguments. On the basis of this corpus, we implement and validate our new framework which combines classification with question answering to analyse arguments in search of misinformation, and provides the explanations in form of critical questions to the human user.
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