Papers › An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

11 Feb 2021EACL 2021 2arXiv:2102.05980archive 2025-07-28

Markus Eberts, Adrian Ulges

We present a joint model for entity-level relation extraction from documents. In contrast to other approaches - which focus on local intra-sentence mention pairs and thus require annotations on mention level - our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. We achieve state-of-the-art relation extraction results on the DocRED dataset and report the first entity-level end-to-end relation extraction results for future reference. Finally, our experimental results suggest that a joint approach is on par with task-specific learning, though more efficient due to shared parameters and training steps.

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