  We introduce translation error correction (TEC), the task of automatically
correcting human-generated translations. Imperfections in machine translations
(MT) have long motivated systems for improving translations post-hoc with
automatic post-editing. In contrast, little attention has been devoted to the
problem of automatically correcting human translations, despite the intuition
that humans make distinct errors that machines would be well-suited to assist
with, from typos to inconsistencies in translation conventions. To investigate
this, we build and release the Aced corpus with three TEC datasets. We show
that human errors in TEC exhibit a more diverse range of errors and far fewer
translation fluency errors than the MT errors in automatic post-editing
datasets, suggesting the need for dedicated TEC models that are specialized to
correct human errors. We show that pre-training instead on synthetic errors
based on human errors improves TEC F-score by as much as 5.1 points. We
conducted a human-in-the-loop user study with nine professional translation
editors and found that the assistance of our TEC system led them to produce
significantly higher quality revised translations.
