The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot classification methods converted the task to other NLP tasks (e.g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data. A potentially valuable by-product of these methods is the large-scale silver standard data, i.e., pseudo-labeled data by the off-the-shelf models of other NLP tasks. However, there is no further investigation into the use of these data. In this paper, we propose a new framework, Clean-LaVe, which aims to utilize silver standard data to enhance the zero-shot performance. Clean-LaVe includes four phases: (1) Obtaining silver data; (2) Identifying relatively clean data from silver data; (3) Finetuning the off-the-shelf model using clean data; (4) Inference on the test data. The experimental results show that Clean-LaVe can outperform the baseline by 5% and 6% on TACRED and Wiki80 dataset in the zero-shot relation classification task, and by 3%-7% on Smile (Korean and Polish) in the zero-shot cross-lingual relation classification task, and by 8% on ACE05-E+ in the zero-shot event argument classification task. The code is share in https://github.com/wjw136/Clean_LaVe.git.

翻译：信息抽取领域中监督分类方法的卓越性能严重依赖于大量金标准数据。近期零样本分类方法将该任务转化为其他自然语言处理任务（如文本蕴含），并直接使用这些任务的现成模型对测试数据进行推理，而无需使用大量信息抽取标注数据。这些方法的一个潜在有价值副产品是大规模银标准数据，即通过其他自然语言处理任务的现成模型生成的伪标签数据。然而，目前尚未对这些数据的使用进行深入研究。本文提出了一种新框架Clean-LaVe，旨在利用银标准数据提升零样本性能。Clean-LaVe包含四个阶段：（1）获取银数据；（2）从银数据中识别相对干净的数据；（3）使用干净数据微调现成模型；（4）对测试数据进行推理。实验结果表明，在零样本关系分类任务中，Clean-LaVe在TACRED和Wiki80数据集上相比基线分别提升5%和6%；在零样本跨语言关系分类任务中，在Smile（韩语和波兰语）数据集上提升3%-7%；在零样本事件论元分类任务中，在ACE05-E+数据集上提升8%。代码共享于https://github.com/wjw136/Clean_LaVe.git。