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.

通过训练评估模型以近似人类评估并强调语义理解，我们成功地探索了一种通过多个数据集进行增强学习，并通过将在一个数据集上训练的评估模型转移到另一个数据集以减少对人工注释数据的依赖的方法，同时还提出了一种弱到强监督的方法，用于使用部分标注数据来训练评估模型，并在训练强化学习模型中实现高性能。