  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 https://github.com/THU-KEG/MAVEN-dataset.
