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

• URL: http://arxiv.org/abs/2102.05980v1
• Date: Thu, 11 Feb 2021 12:49:39 GMT
• Title: An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
• Authors: Markus Eberts, Adrian Ulges
• Abstract summary: We present a joint model for entity-level relation extraction from documents. We achieve state-of-the-art relation extraction results on the DocRED dataset. 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.
• Score: 2.111790330664657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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