  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.
