Quantum mechanics

Voltage

Engineering

Generative model

Materials science

Event (particle physics)

Systems engineering

Computer science

Noun phrase

Resolution (logic)

Natural language processing

Physics

Generative grammar

Named-entity recognition

Task (project management)

Filler (materials)

Noun

Knowledge base

Entity linking

Artificial intelligence

Composite material

Sentence

Transformer

Coreference

Electrical engineering

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.

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