A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction

Understand

In knowledge graph construction, a challenging issue is how to extract complex (e.g., overlapping) entities and relationships from a small amount of unstructured historical data.

• The traditional pipeline methods are to divide the extraction into two separate subtasks, which misses the potential interaction between the two subtasks and may lead to error propagation.
• In this work, we propose an effective cascade dual-decoder method to extract overlapping relational triples, which includes a text-specific relation decoder and a relation-corresponded entity decoder.
• Our approach is straightforward and it includes a text-specific relation decoder and a relation-corresponded entity decoder.