A Frustratingly Easy Approach for Joint Entity and Relation Extraction
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End-to-end relation extraction aims to identify named entities and extract relations between them simultaneously. Most recent work models these two subtasks jointly, either by unifying them in one structured prediction framework, or multi-task learning through shared representations. In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05, and SciERC). Our approach essentially builds on two independent pre-trained encoders and merely uses the entity model to provide input features for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information at the input layer of the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both encoders at inference time, obtaining a 8-16× speedup with a small accuracy drop.
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