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Event extraction (EE) is a crucial information extraction task that aims toextract event information in texts. Most existing methods assume that eventsappear in sentences without overlaps, which are not applicable to thecomplicated overlapping event extraction. This work systematically studies therealistic event overlapping problem, where a word may serve as triggers withseveral types or arguments with different roles. To tackle the above problem,we propose a novel joint learning framework with cascade decoding foroverlapping event extraction, termed as CasEE. Particularly, CasEE sequentiallyperforms type detection, trigger extraction and argument extraction, where theoverlapped targets are extracted separately conditioned on the specific formerprediction. All the subtasks are jointly learned in a framework to capturedependencies among the subtasks. The evaluation on a public event extractionbenchmark FewFC demonstrates that CasEE achieves significant improvements onoverlapping event extraction over previous competitive methods.

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction