Event　extraction　aims　to　recognize　pre-defined　event　triggers　and　arguments　from　texts,　which　suffer　from　the　lack　of　high-quality　annotations.　In　most　NLP　applications,　involving　a　large　scale　of　synthetic　training　data　is　a　practical　and　effective　approach　to　alleviate　the　problem　of　data　scarcity.　However,　when　applying　to　the　task　of　event　extraction,　recent　data　augmentation　methods　often　neglect　the　problem　of　grammatical　incorrectness,　structure　misalignment,　and　semantic　drifting,　leading　to　unsatisfactory　performances.　In　order　to　solve　these　problems,　we　propose　a　denoised　structure-to-text　augmentation　framework　for　event　extraction　(DAEE),　which　generates　additional　training　data　through　the　knowledge-based　structure-to-text　generation　model　and　selects　the　effective　subset　from　the　generated　data　iteratively　with　a　deep　reinforcement　learning　agent.　Experimental　results　on　several　datasets　demonstrate　that　the　proposed　method　generates　more　diverse　text　representations　for　event　extraction　and　achieves　comparable　results　with　the　state-of-the-art.　©　2023　Association　for　Computational　Linguistics.