  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:
https://github.com/YukinoWan/kNN-RE.
