Event detection (ED), which means identifying event trigger words and
classifying event types, is the first and most fundamental step for extracting
event knowledge from plain text. Most existing datasets exhibit the following
issues that limit further development of ED: (1) Data scarcity. Existing
small-scale datasets are not sufficient for training and stably benchmarking
increasingly sophisticated modern neural methods. (2) Low coverage. Limited
event types of existing datasets cannot well cover general-domain events, which
restricts the applications of ED models. To alleviate these problems, we
present a MAssive eVENt detection dataset (MAVEN), which contains 4,480
Wikipedia documents, 118,732 event mention instances, and 168 event types.
MAVEN alleviates the data scarcity problem and covers much more general event
types. We reproduce the recent state-of-the-art ED models and conduct a
thorough evaluation on MAVEN. The experimental results show that existing ED
methods cannot achieve promising results on MAVEN as on the small datasets,
which suggests that ED in the real world remains a challenging task and
requires further research efforts. We also discuss further directions for
general domain ED with empirical analyses. The source code and dataset can be
obtained from this https URL

本研究提出了一种包含 4480 份维基百科文件、118732 次事件提及实例和 168 种事件类型的 MAVEN 数据集，用于缓解数据不足和低覆盖率等问题，实证分析了通用领域事件检测中的进一步研究方向，并表明现有的事件检测方法不能像小规模数据集中表现出的那样在 MAVEN 上表现出色，说明其在现实世界中仍然是具有挑战性的任务，需要进一步的研究和努力。