  The superior performance of supervised relation extraction (RE) methods
heavily relies on a large amount of gold standard data. Recent zero-shot
relation extraction methods converted the RE task to other NLP tasks and used
off-the-shelf models of these NLP tasks to directly perform inference on the
test data without using a large amount of RE annotation data. A potentially
valuable by-product of these methods is the large-scale silver standard data.
However, there is no further investigation on the use of potentially valuable
silver standard data. In this paper, we propose to first detect a small amount
of clean data from silver standard data and then use the selected clean data to
finetune the pretrained model. We then use the finetuned model to infer
relation types. We also propose a class-aware clean data detection module to
consider class information when selecting clean data. The experimental results
show that our method can outperform the baseline by 12% and 11% on TACRED and
Wiki80 dataset in the zero-shot RE task. By using extra silver standard data of
different distributions, the performance can be further improved.
