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
this https URL

该论文提出了一种新的框架 Clean-LaVe，旨在利用银标数据提高零样本性能。该框架在零样本关系分类任务上超过基线 5% 和 6%，在零样本跨语言关系分类任务上超过基线 3%-7%，在零样本事件参数分类任务上超过基线 8%。

信息抽取中使用银标准数据进行零样本分类任务的研究

On the use of Silver Standard Data for Zero-shot Classification Tasks in  Information Extraction

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.

本文通过使用 zero-shot relation extraction 方法获得 silver standard data，提出了使用 clean data detection module 模块从中识别出 clean data，并使用 finetuned 模型推断 relation types，实验结果在 TACRED 和 Wiki80 数据集上实现了比基准线更好的 12% 和 11% 的效果。