Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between these events, recent efforts leverage the idea of "memory", where the results of already predicted events are cached and can be retrieved to help the prediction of upcoming events. These methods extract events according to their appearance order in the document, however, the event that appears in the first sentence does not mean that it is the easiest to extract. Existing methods might introduce noise to the extraction of upcoming events if they rely on an incorrect prediction of previous events. In order to provide more reliable memory, we propose a simple-to-complex progressive framework for document-level EAE. Specifically, we first calculate the difficulty of each event and then, we conduct the extraction following a simple-to-complex order. In this way, the memory will store the most certain results, and the model could use these reliable sources to help the prediction of more difficult events. Experiments on WikiEvents show that our model outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex framework is useful in the EAE task.

翻译：文档级事件论元抽取（Event Argument Extraction, EAE）要求模型从单个文档中提取多个事件的论元。考虑到这些事件之间潜在的依赖关系，近期研究采用“记忆”机制，将已预测事件的结果缓存并检索以辅助后续事件的预测。然而，现有方法按事件在文档中出现的顺序进行抽取，但出现在首句的事件并不一定是最易抽取的。若依赖先前事件的错误预测，这些方法可能对后续事件的抽取引入噪声。为提供更可靠的记忆，我们提出一种从简单到复杂的渐进式文档级EAE框架。具体而言，我们首先计算每个事件的难度，再按照从简单到复杂的顺序进行抽取。通过这种方式，记忆将存储最确定的结果，模型可利用这些可靠来源辅助预测更困难的事件。在WikiEvents数据集上的实验表明，我们的模型在F1值上超越当前最优方法1.4%，证明所提出的从简单到复杂框架在EAE任务中具有有效性。