Relation extraction (RE) has achieved remarkable progress with the help of
pre-trained language models. However, existing RE models are usually incapable
of handling two situations: implicit expressions and long-tail relation types,
caused by language complexity and data sparsity. In this paper, we introduce a
simple enhancement of RE using $k$ nearest neighbors ($k$NN-RE). $k$NN-RE
allows the model to consult training relations at test time through a
nearest-neighbor search and provides a simple yet effective means to tackle the
two issues above. Additionally, we observe that $k$NN-RE serves as an effective
way to leverage distant supervision (DS) data for RE. Experimental results show
that the proposed $k$NN-RE achieves state-of-the-art performances on a variety
of supervised RE datasets, i.e., ACE05, SciERC, and Wiki80, along with
outperforming the best model to date on the i2b2 and Wiki80 datasets in the
setting of allowing using DS. Our code and models are available at:
this https URL.

介绍一种使用 $k$ 最近邻进行增强的关系提取方法 $k$NN-RE，可以在测试时间通过最近邻搜索查阅训练关系，并提供了一种简单却有效的方法来解决由于语言复杂性和数据稀疏性引起的隐式表达和长尾关系类型的两个问题，并且观察到 $k$NN-RE 是利用远程监督（DS）数据进行 RE 的有效方法。在 ACE05、SciERC 和 Wiki80 等多种受控制的 RE 数据集上，提出的 $k$NN-RE 取得了最高水平的性能，并在允许使用 DS 的情况下在 i2b2 和 Wiki80 数据集上优于迄今为止最好的模型。