  Event coreference resolution(ECR) is an important task in Natural Language
Processing (NLP) and nearly all the existing approaches to this task rely on
event argument information. However, these methods tend to suffer from error
propagation from the stage of event argument extraction. Besides, not every
event mention contains all arguments of an event, and argument information may
confuse the model that events have arguments to detect event coreference in
real text. Furthermore, the context information of an event is useful to infer
the coreference between events. Thus, in order to reduce the errors propagated
from event argument extraction and use context information effectively, we
propose a multi-loss neural network model that does not need any argument
information to do the within-document event coreference resolution task and
achieve a significant performance than the state-of-the-art methods.
