  This paper introduces the task of factual error correction: performing edits
to a claim so that the generated rewrite is better supported by evidence. This
extends the well-studied task of fact verification by providing a mechanism to
correct written texts that are refuted or only partially supported by evidence.
We demonstrate that it is feasible to train factual error correction systems
from existing fact checking datasets which only contain labeled claims
accompanied by evidence, but not the correction. We achieve this by employing a
two-stage distant supervision approach that incorporates evidence into masked
claims when generating corrections. Our approach, based on the T5 transformer
and using retrieved evidence, achieved better results than existing work which
used a pointer copy network and gold evidence, producing accurate factual error
corrections for 5x more instances in human evaluation and a .125 increase in
SARI score. The evaluation is conducted on a dataset of 65,000 instances based
on a recent fact verification shared task and we release it to enable further
work on the task.
