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\times speedup with a small accuracydrop .
End-to-end relation extraction aims to identify named entities and extract relations between them . Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC) , obtaining a 1.7\%-2.8\% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders . Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input 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 early in the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, achieving an 8-16\times speedup with a slight reduction in accuracy .
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simultaneously
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unifying
casting
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performing
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describe a very simple approach for joint
present a simple pipelined approach for
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,
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, obtaining a 1.7\%-2.8\% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders
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pre-trained
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provide input features
construct the input
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at the input layer of the
early in the
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entity and relation
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obtaining a
achieving an
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small accuracydrop
slight reduction in accuracy