  Classifiers commonly make use of pre-annotated datasets, wherein a model is
evaluated by pre-defined metrics on a held-out test set typically made of
human-annotated labels. Metrics used in these evaluations are tied to the
availability of well-defined ground truth labels, and these metrics typically
do not allow for inexact matches. These noisy ground truth labels and strict
evaluation metrics may compromise the validity and realism of evaluation
results. In the present work, we discuss these concerns and conduct a
systematic posthoc verification experiment on the entity linking (EL) task.
Unlike traditional methodologies, which asks annotators to provide free-form
annotations, we ask annotators to verify the correctness of annotations after
the fact (i.e., posthoc). Compared to pre-annotation evaluation,
state-of-the-art EL models performed extremely well according to the posthoc
evaluation methodology. Posthoc validation also permits the validation of the
ground truth dataset. Surprisingly, we find predictions from EL models had a
similar or higher verification rate than the ground truth. We conclude with a
discussion on these findings and recommendations for future evaluations.
