Simple BERT Models for Relation Extraction and Semantic Role Labeling

Peng Shi; Jimmy Lin

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arXiv · 1904.05255

Simple BERT Models for Relation Extraction and Semantic Role Labeling

Peng Shi·Jimmy Lin

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Abstract

We present simple BERT-based models for relation extraction and semantic role labeling. In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. In this paper, extensive experiments on datasets for these two tasks show that without using any external features, a simple BERT-based model can achieve state-of-the-art performance. To our knowledge, we are the first to successfully apply BERT in this manner. Our models provide strong baselines for future research.

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Peng Shi, Jimmy Lin. 2019-04-10. Simple BERT Models for Relation Extraction and Semantic Role Labeling. https://arxiv.org/abs/1904.05255

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