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

通过研究数据采集和注释过程中的分布偏差，我们提出了一种称为 Challenging RE (CRE) 的挑战性数据集，分析了四个最先进的关系抽取 (RE) 模型的失败模式，挖掘了它们浅层启发式算法的局限性，并发现替代的问答建模表现显著更好，提供了改进数据收集的具体建议。