This paper presents a question-answering approach to extract document-level
event-argument structures. We automatically ask and answer questions for each
argument type an event may have. Questions are generated using manually defined
templates and generative transformers. Template-based questions are generated
using predefined role-specific wh-words and event triggers from the context
document. Transformer-based questions are generated using large language models
trained to formulate questions based on a passage and the expected answer.
Additionally, we develop novel data augmentation strategies specialized in
inter-sentential event-argument relations. We use a simple span-swapping
technique, coreference resolution, and large language models to augment the
training instances. Our approach enables transfer learning without any
corpora-specific modifications and yields competitive results with the RAMS
dataset. It outperforms previous work, and it is especially beneficial to
extract arguments that appear in different sentences than the event trigger. We
also present detailed quantitative and qualitative analyses shedding light on
the most common errors made by our best model.

本文提出了一种问答方法来提取文档级别的事件 - 论证结构，使用手动定义的模板和生成型 Transformer 自动生成问题，并使用数据增强策略和迁移学习提高抽取结果的准确性，同时对最佳模型的错误进行详细分析。