  Event Extraction (EE) is an essential information extraction task that aims
to extract event-related information from unstructured texts. The paradigm of
this task has shifted from conventional classification-based methods to more
contemporary question-answering-based (QA-based) approaches. However, in
QA-based EE, the quality of the questions dramatically affects the extraction
accuracy, and how to generate high-quality questions for QA-based EE remains a
challenge. In this work, to tackle this challenge, we suggest four criteria to
evaluate the quality of a question and propose a reinforcement learning method,
RLQG, for QA-based EE that can generate generalizable, high-quality, and
context-dependent questions and provides clear guidance to QA models. The
extensive experiments conducted on ACE and RAMS datasets have strongly
validated our approach's effectiveness, which also demonstrates its robustness
in scenarios with limited training data. The corresponding code of RLQG is
released for further research.
