  This paper introduces the task of factual error correction: performing edits
to a claim so that the generated rewrite is supported by evidence. This serves
two purposes: firstly this provides a mechanism to correct written texts that
contain misinformation, and secondly, this acts as an inherent explanation for
claims already partially supported by evidence. We demonstrate that factual
error correction is possible without the need for any additional training data
using distant-supervision and retrieved evidence. We release a dataset of
65,000 instances, based on a recent fact verification dataset, to compare our
distantly-supervised method to a fully supervised ceiling system. Our manual
evaluation indicates which automated evaluation metrics best correlate with
human judgements of factuality and whether errors were actually corrected.
