  The inherent ambiguity of cause and effect boundaries poses a challenge in
evaluating causal event extraction tasks. Traditional metrics like Exact Match
and BertScore poorly reflect model performance, so we trained evaluation models
to approximate human evaluation, achieving high agreement. We used them to
perform Reinforcement Learning with extraction models to align them with human
preference, prioritising semantic understanding. We successfully explored our
approach through multiple datasets, including transferring an evaluator trained
on one dataset to another as a way to decrease the reliance on human-annotated
data. In that vein, we also propose a weak-to-strong supervision method that
uses a fraction of the annotated data to train an evaluation model while still
achieving high performance in training an RL model. Our code is available at
https://github.com/oyarsa/event_extraction/tree/causal-event-extraction.
