Implicit event argument extraction (EAE) aims to identify arguments that
could scatter over the document. Most previous work focuses on learning the
direct relations between arguments and the given trigger, while the implicit
relations with long-range dependency are not well studied. Moreover, recent
neural network based approaches rely on a large amount of labeled data for
training, which is unavailable due to the high labelling cost. In this paper,
we propose a Curriculum learning based Prompt tuning (CUP) approach, which
resolves implicit EAE by four learning stages. The stages are defined according
to the relations with the trigger node in a semantic graph, which well captures
the long-range dependency between arguments and the trigger. In addition, we
integrate a prompt-based encoder-decoder model to elicit related knowledge from
pre-trained language models (PLMs) in each stage, where the prompt templates
are adapted with the learning progress to enhance the reasoning for arguments.
Experimental results on two well-known benchmark datasets show the great
advantages of our proposed approach. In particular, we outperform the
state-of-the-art models in both fully-supervised and low-data scenarios.

本篇论文通过在语义图中定义阶段和适应学习进展的提示模板，提出了一种基于课程学习的提示微调方法，结合预先训练的语言模型，解决了跨文本范围内的隐性事件参数提取问题，并在两个基准测试数据集中都表现优异。