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We revisit the classic problem of document-level role-filler entityextraction (REE) for template filling. We argue that sentence-level approachesare ill-suited to the task and introduce a generative transformer-basedencoder-decoder framework (GRIT) that is designed to model context at thedocument level: it can make extraction decisions across sentence boundaries; isimplicitly aware of noun phrase coreference structure, and has the capacity torespect cross-role dependencies in the template structure. We evaluate ourapproach on the MUC-4 dataset, and show that our model performs substantiallybetter than prior work. We also show that our modeling choices contribute tomodel performance, e.g., by implicitly capturing linguistic knowledge such asrecognizing coreferent entity mentions.

GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction