论文标题
收敛到事实：通过迭代限制编辑纠正事实误差
Converge to the Truth: Factual Error Correction via Iterative Constrained Editing
论文作者
论文摘要
鉴于可能是错误的索赔句，我们如何通过最小的编辑自动纠正它？现有方法要么需要大量的虚假和校正后的索赔来进行监督培训，要么无法处理跨越话语中多个令牌的良好错误。在本文中，我们提出了Vence，这是一种用最少编辑的事实误差校正（FEC）的新方法。 Vence将FEC问题制定为相对于目标密度函数的迭代采样编辑作用。我们通过从离线训练的事实验证模型中仔细设计目标函数。 Vence根据输入令牌的真实性得分的后置梯度和使用遥远的监督语言模型（T5）的编辑操作进行了最可能的编辑位置。公共数据集上的实验表明，Vence比以前的最佳远程监督方法将良好的纱丽指标提高了5.3（或相对改善11.8％）。
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.
