  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.
