Supervised relation extraction methods based on deep neural network play an
important role in the recent information extraction field. However, at present,
their performance still fails to reach a good level due to the existence of
complicated relations. On the other hand, recently proposed pre-trained
language models (PLMs) have achieved great success in multiple tasks of natural
language processing through fine-tuning when combined with the model of
downstream tasks. However, original standard tasks of PLM do not include the
relation extraction task yet. We believe that PLMs can also be used to solve
the relation extraction problem, but it is necessary to establish a specially
designed downstream task model or even loss function for dealing with
complicated relations. In this paper, a new network architecture with a special
loss function is designed to serve as a downstream model of PLMs for supervised
relation extraction. Experiments have shown that our method significantly
exceeded the current optimal baseline models across multiple public datasets of
relation extraction.

本文提出了一种新的神经网络结构和特殊的损失函数来提高细化语言模型（PLMs）在监督关系抽取中的性能。实验证明，该方法在多项公共数据集上显著超过了当前最优基线模型。