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.

本文提出了一种新的方法VENCE用于事实错误的纠正，通过设计离线训练的事实验证模型的预测准确性得分，使用迭代采样编辑操作的目标密度函数，基于T5距离监督语言模型的最有可能的编辑位置进行采样，实现了事实错误纠正，实验结果表明在公共数据集上，相比之前最好的远程监督方法，VENCE相对改进了11.8%的SARI指标。

收敛到真相：通过迭代约束编辑进行事实错误纠正