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

Converge to the Truth: Factual Error Correction via Iterative Constrained Editing