LEVEN : A Large-Scale C hinese Legal Event Detection Dataset (2022)

Abstract: 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 .

## introduction finding out the occurred events and causal relations between them is fundamental to analyzing legal cases and making judgments. legal event detection (led) aims to automatically extract the ## data collection our ultimate goal is to construct a large-scale legal event detection dataset with a high-coverage event type schema and sufficient event instances, which is scarce in existing led datasets. therefore, we need to redefine a new event schema, select the trigger candidates, and annotate the corresponding event types. as criminal cases usually involve principal rights and complex facts, we focus on criminal legal events in this paper. in the following sections, we first introduce the construction of event schema and then describe the process of annotation of candidates and related event types. ## data analysis in this section, we conduct analysis from various aspects to provide a deep understanding of leven. ## conclusion and future work in this paper, we construct the largest legal event detection dataset, leven, which contains a comprehensive legal event schema and hundreds of thousands of event mentions. we evaluate several competitive baseline models and conduct error analysis for these models on leven. the experimental results prove that it still needs future efforts to promote the development of led. furthermore, we employ led for downstream legal document analysis, including legal judgment prediction and similar case retrieval. it indicates that led can provide fine-grained information and serve as a fundamental process for legal artificial intelligence. in the future, we will explore to conduct more analysis on large-scale legal documents based on the event information, and annotate leven with event relations and event arguments.