Annotating text data for event information extraction systems is hard,
expensive, and error-prone. We investigate the feasibility of integrating
coarse-grained data (document or sentence labels), which is far more feasible
to obtain, instead of annotating more documents. We utilize a multi-task model
with two auxiliary tasks, document and sentence binary classification, in
addition to the main task of token classification. We perform a series of
experiments with varying data regimes for the aforementioned integration.
Results show that while introducing extra coarse-grained data offers greater
improvement and robustness, a gain is still possible with only the addition of
negative documents that have no information on any event.

本文研究了使用粗粒度数据（文档或句子标签）替代对文档进行标注以进行事件信息提取的可行性，证明了使用多任务模型和二元分类等技术，即使只增加对没有事件信息的负面文档的标注也能提高提取性能。

在低数据设置中利用粗粒度数据进行事件提取

Utilizing coarse-grained data in low-data settings for event extraction

We ask whether text understanding has progressed to where we may extract
event information through incremental refinement of bleached statements derived
from annotation manuals. Such a capability would allow for the trivial
construction and extension of an extraction framework by intended end-users
through declarations such as, "Some person was born in some location at some
time." We introduce an example of a model that employs such statements, with
experiments illustrating we can extract events under closed ontologies and
generalize to unseen event types simply by reading new definitions.

该研究旨在验证文本理解是否已经发展到可以通过注释手册衍生出精炼的陈述来提取事件信息的程度，通过引入一个采用这种陈述的模型示例，实验表明我们可以在封闭本体中提取事件，并通过阅读新的定义来概括未见过的事件类型。