Extracting entities and relations from unstructured text has attracted
increasing attention in recent years but remains challenging, due to the
intrinsic difficulty in identifying overlapping relations with shared entities.
Prior works show that joint learning can result in a noticeable performance
gain. However, they usually involve sequential interrelated steps and suffer
from the problem of exposure bias. At training time, they predict with the
ground truth conditions while at inference it has to make extraction from
scratch. This discrepancy leads to error accumulation. To mitigate the issue,
we propose in this paper a one-stage joint extraction model, namely, TPLinker,
which is capable of discovering overlapping relations sharing one or both
entities while immune from the exposure bias. TPLinker formulates joint
extraction as a token pair linking problem and introduces a novel handshaking
tagging scheme that aligns the boundary tokens of entity pairs under each
relation type. Experiment results show that TPLinker performs significantly
better on overlapping and multiple relation extraction, and achieves
state-of-the-art performance on two public datasets.

本文提出了一种名为 TPLinker 的单阶段联合提取模型，其能够发现共享一个或两个实体的重叠关系，同时免除暴露偏差的问题。该模型通过手部握手标记方案将关系类型下的实体对的边界标记对齐，实验结果表明该模型在多关系抽取方面表现显著优于基准方法，并在两个公共数据集上实现了最先进的性能。