We present a simple yet effective self-training approach, named as STAD, for
low-resource relation extraction. The approach first classifies the
auto-annotated instances into two groups: confident instances and uncertain
instances, according to the probabilities predicted by a teacher model. In
contrast to most previous studies, which mainly only use the confident
instances for self-training, we make use of the uncertain instances. To this
end, we propose a method to identify ambiguous but useful instances from the
uncertain instances and then divide the relations into candidate-label set and
negative-label set for each ambiguous instance. Next, we propose a set-negative
training method on the negative-label sets for the ambiguous instances and a
positive training method for the confident instances. Finally, a joint-training
method is proposed to build the final relation extraction system on all data.
Experimental results on two widely used datasets SemEval2010 Task-8 and
Re-TACRED with low-resource settings demonstrate that this new self-training
approach indeed achieves significant and consistent improvements when comparing
to several competitive self-training systems. Code is publicly available at
this https URL

本文提出了一种名为 STAD 的简单而有效的自助式训练方法，用于低资源关系提取，该方法利用模糊但有用的实例，并针对模棱两可的实例将关系划分为候选标签集合和负标签集合，并最终在所有数据上建立最终的关系提取系统。实验结果表明，与几个竞争自训练系统相比，此新型自训练方法确实取得了显着和一致的改进。