We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our approach employs the pre-trained Transformer network BERT as its core. We use BERT embeddings as shared inputs for a light-weight reasoning, which features entity recognition and filtering, as well as relation classification with a localized, marker-free context representation. The model is trained on strong within-sentence negative samples, which are efficiently extracted in a single BERT pass. These aspects facilitate a search over all spans in the sentence. In ablation studies, we demonstrate the benefits of pre-training, strong negative sampling and localized context. Our model outperforms prior work by up to 5% F1 score on several datasets for joint entity and relation extraction.

翻译：我们引入了SpERT, 一种基于跨边界的联合实体和关系提取的注意模式。 我们的方法将预先训练的变异网络BERT作为核心。 我们使用BERT嵌入作为轻量级推理的共享投入, 其特点是实体识别和过滤, 以及与本地化的无标记背景代表关系分类。 该模型在强大的判决内负样本方面受过培训, 这些样本在单一的BERT通行证中有效提取。 这些方面有利于对句子中的所有内容进行搜索。 在通缩研究中, 我们展示了培训前、 强烈的负面抽样和本地化背景的好处。 我们的模型比先前在联合实体和关系提取的若干数据集上的工作高出高达5%的F1分。