MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

TL;DR

MAVEN-ERE is a large-scale, unified dataset for event coreference, temporal, causal, and subevent relation extraction, enabling comprehensive training and evaluation of models on diverse event relations.

Contribution

The paper introduces MAVEN-ERE, the largest unified dataset for multiple event relations, with improved annotation schemes and extensive coverage, addressing limitations of existing datasets.

Findings

Joint learning of relation types improves extraction performance.

The dataset is more challenging and comprehensive than previous datasets.

Models benefit from the interaction information among different event relations.

Abstract

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103,193 event coreference chains, 1,216,217 temporal relations, 57,992 causal relations, and 15,841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://github.com/THU-KEG/MAVEN-ERE.