Given a possibly false claim sentence, how can we automatically correct it with minimal editing? Existing methods either require a large number of pairs of false and corrected claims for supervised training or do not handle well errors spanning over multiple tokens within an utterance. In this paper, we propose VENCE, a novel method for factual error correction (FEC) with minimal edits. VENCE formulates the FEC problem as iterative sampling editing actions with respect to a target density function. We carefully design the target function with predicted truthfulness scores from an offline trained fact verification model. VENCE samples the most probable editing positions based on back-calculated gradients of the truthfulness score concerning input tokens and the editing actions using a distantly-supervised language model (T5). Experiments on a public dataset show that VENCE improves the well-adopted SARI metric by 5.3 (or a relative improvement of 11.8%) over the previous best distantly-supervised methods.

翻译：鉴于可能的虚假索赔判决,我们如何用最低限度的编辑来自动纠正它?现有的方法要么需要大量虚假和纠正的索赔要求,以接受监督的培训,要么不处理一个语句中跨越多个符号的错误。在本文中,我们建议VENE,这是对事实错误进行纠正的新办法,只有最低限度的编辑。VENE将FEC问题描述为对目标密度函数的迭代抽样编辑操作。我们仔细设计目标函数,从一个离线培训的事实核查模型中预测真实性得分。VENE根据输入符号的真实性评分的后算梯度,以及使用远视语言模型(T5)进行的编辑动作,对VENE的实验表明,VENG比以往最远超前采用的方法改进了5.3的采用良好SARI指标(或相对改进11.8%)。