  Event extraction (EE) is a crucial information extraction task that aims to
extract event information in texts. Most existing methods assume that events
appear in sentences without overlaps, which are not applicable to the
complicated overlapping event extraction. This work systematically studies the
realistic event overlapping problem, where a word may serve as triggers with
several types or arguments with different roles. To tackle the above problem,
we propose a novel joint learning framework with cascade decoding for
overlapping event extraction, termed as CasEE. Particularly, CasEE sequentially
performs type detection, trigger extraction and argument extraction, where the
overlapped targets are extracted separately conditioned on the specific former
prediction. All the subtasks are jointly learned in a framework to capture
dependencies among the subtasks. The evaluation on a public event extraction
benchmark FewFC demonstrates that CasEE achieves significant improvements on
overlapping event extraction over previous competitive methods.
