REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
This addresses the need for efficient and accurate structured information extraction at web-scale for NLP applications, though it is incremental as it builds on existing joint task approaches.
The paper tackles the problem of closed information extraction from documents by proposing REXEL, an end-to-end model that jointly performs mention detection, entity typing, entity disambiguation, coreference resolution, and document-level relation classification, achieving an average 11 times faster inference and surpassing baselines by over 6 F1 points.
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). REXEL performs mention detection, entity typing, entity disambiguation, coreference resolution and document-level relation classification in a single forward pass to yield facts fully linked to a reference knowledge graph. It is on average 11 times faster than competitive existing approaches in a similar setting and performs competitively both when optimised for any of the individual subtasks and a variety of combinations of different joint tasks, surpassing the baselines by an average of more than 6 F1 points. The combination of speed and accuracy makes REXEL an accurate cost-efficient system for extracting structured information at web-scale. We also release an extension of the DocRED dataset to enable benchmarking of future work on DocIE, which is available at https://github.com/amazon-science/e2e-docie.