  Recognizing facts is the most fundamental step in making judgments, hence
detecting events in the legal documents is important to legal case analysis
tasks. However, existing Legal Event Detection (LED) datasets only concern
incomprehensive event types and have limited annotated data, which restricts
the development of LED methods and their downstream applications. To alleviate
these issues, we present LEVEN a large-scale Chinese LEgal eVENt detection
dataset, with 8,116 legal documents and 150,977 human-annotated event mentions
in 108 event types. Not only charge-related events, LEVEN also covers general
events, which are critical for legal case understanding but neglected in
existing LED datasets. To our knowledge, LEVEN is the largest LED dataset and
has dozens of times the data scale of others, which shall significantly promote
the training and evaluation of LED methods. The results of extensive
experiments indicate that LED is challenging and needs further effort.
Moreover, we simply utilize legal events as side information to promote
downstream applications. The method achieves improvements of average 2.2 points
precision in low-resource judgment prediction, and 1.5 points mean average
precision in unsupervised case retrieval, which suggests the fundamentality of
LED. The source code and dataset can be obtained from
https://github.com/thunlp/LEVEN.
