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The process of collecting and annotating training data may introducedistribution artifacts which may limit the ability of models to learn correctgeneralization behavior. We identify failure modes of SOTA relation extraction(RE) models trained on TACRED, which we attribute to limitations in the dataannotation process. We collect and annotate a challenge-set we call ChallengingRE (CRE), based on naturally occurring corpus examples, to benchmark thisbehavior. Our experiments with four state-of-the-art RE models show that theyhave indeed adopted shallow heuristics that do not generalize to thechallenge-set data. Further, we find that alternative question answeringmodeling performs significantly better than the SOTA models on thechallenge-set, despite worse overall TACRED performance. By adding some of thechallenge data as training examples, the performance of the model improves.Finally, we provide concrete suggestion on how to improve RE data collection toalleviate this behavior.

Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data