An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
Markus Eberts, Adrian Ulges · 2021
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-theart relation extraction results on the DocRED dataset and report the first entity-level end-toend relation extraction results for future reference.Finally, our experimental results suggest that a joint approach is on par with taskspecific learning, though more efficient due to shared parameters and training steps.