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Manually annotated datasets are crucial for training and evaluating NaturalLanguage Processing models. However, recent work has discovered that evenwidely-used benchmark datasets contain a substantial number of erroneousannotations. This problem has been addressed with Annotation Error Detection(AED) models, which can flag such errors for human re-annotation. However, eventhough many of these AED methods assume a final curation step in which a humanannotator decides whether the annotation is erroneous, they have been developedas static models without any human-in-the-loop component. In this work, wepropose ActiveAED, an AED method that can detect errors more accurately byrepeatedly querying a human for error corrections in its prediction loop. Weevaluate ActiveAED on eight datasets spanning five different tasks and findthat it leads to improvements over the state of the art on seven of them, withgains of up to six percentage points in average precision.

ActiveAED: A Human in the Loop Improves Annotation Error Detection