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

Understand

We revisit the classic problem of document-level role-filler entity extraction (REE) for template filling.

• We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoder-decoder framework (GRIT) that is designed to model context at the document level: it can make extraction decisions across sentence boundaries; is implicitly aware of noun phrase coreference structure, and has the capacity to respect cross-role dependencies in the template structure.
• We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work.
• We also show that our modeling choices contribute to model performance, e.g., by implicitly capturing linguistic knowledge such as recognizing coreferent entity mentions.