Data Augmentation with Reinforcement Learning for Document-Level Event Coreference Resolution

Most previous models on event coreference resolution largely depend on hand-crafted features and annotated corpora. To address above issues, this paper introduces a neural model to resolve document-level event coreference in raw texts by both employing various neural components to better represent event semantics and integrating data augmentation with reinforcement learning to largely expand the dataset and effectively improve its quality. Experimentation on three KBP datasets shows that our proposed neural model significantly outperforms several strong state-of-the-art baselines.