Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and our code can be obtained from https://github.com/THU-KEG/MAVEN-Argument.

翻译：理解文本中的事件是自然语言理解的核心目标，这需要检测事件发生、提取事件论元以及分析事件间关系。然而，由于任务复杂性带来的标注挑战，长期以来一直缺乏覆盖事件理解全流程的大规模数据集。本文提出MAVEN-Arg，该数据集通过为MAVEN数据集补充事件论元标注，成为首个支持事件检测、事件论元提取和事件关系抽取的一体化数据集。作为事件论元提取基准，MAVEN-Arg具有三大优势：（1）完整的事件论元体系覆盖162种事件类型和612种论元角色，所有类型及角色均附有专家编写的定义和示例；（2）大规模数据量，包含通过人工标注获得的98,591个事件和290,613个论元；（3）支持事件论元提取所有任务变体的穷尽式标注，在文档层面同时标注实体论元与非实体论元。实验表明，MAVEN-Arg对微调事件论元提取模型和专有大型语言模型均具有较大挑战性。此外，为展示一体化数据集的价值，我们初步探索了基于大型语言模型的潜在应用——未来事件预测。MAVEN-Arg及代码可从https://github.com/THU-KEG/MAVEN-Argument获取。