Relation tuple extraction from text is an important task for building
knowledge bases. Recently, joint entity and relation extraction models have
achieved very high F1 scores in this task. However, the experimental settings
used by these models are restrictive and the datasets used in the experiments
are not realistic. They do not include sentences with zero tuples
(zero-cardinality). In this paper, we evaluate the state-of-the-art joint
entity and relation extraction models in a more realistic setting. We include
sentences that do not contain any tuples in our experiments. Our experiments
show that there is significant drop ($\sim 10-15\%$ in one dataset and $\sim
6-14\%$ in another dataset) in their F1 score in this setting. We also propose
a two-step modeling using a simple BERT-based classifier that leads to
improvement in the overall performance of these models in this realistic
experimental setup.

本文中，我们通过增加零基数句子，对最先进的实体和关系提取模型进行了更加实际的实验设置与评估，结果表明这种情况下模型的 F1 得分明显下降，提出了基于 BERT 的两步建模方案，可以提高模型在这种实际情况下的性能。