Recent years have seen a gradual shift of focus from entity-based tasks to event-based tasks
in information extraction research. Being a core event-based task, event coreference …

Despite recent promising results on the application of span-based models for event
reference interpretation, there is a lack of understanding of what has been improved. We …

While unsupervised anaphoric zero pronoun (AZP) resolvers have recently been shown to
rival their supervised counterparts in performance, it is relatively difficult to scale them up to …

Event coreference resolution is an important research problem with many applications.
Despite the recent remarkable success of pretrained language models, we argue that it is …

Conventional event coreference systems commonly use a pipeline architecture and rely
heavily on handcrafted features, which often causes error propagation problems and leads …

Event coreference resolution is a challenging problem since it relies on several components
of the information extraction pipeline that typically yield noisy outputs. We hypothesize that …

Event extraction has become one of the most important topics in information extraction, but
to date, there is very limited work on leveraging cross-lingual training to boost performance …

We address two key challenges in end-to-end event coreference resolution research:(1) the
error propagation problem, where an event coreference resolver has to assume as input the …

Motivated by the recent successful application of span-based models to entity-based
information extraction tasks, we investigate span-based models for event coreference …

Multi-pass sieve approaches have been successfully applied to entity coreference resolution
and many other tasks in natural language processing (NLP), owing in part to the ease of …