In this paper, we propose a novel method for joint entity and relation
extraction from unstructured text by framing it as a conditional sequence
generation problem. In contrast to conventional generative information
extraction models that are left-to-right token-level generators, our approach
is \textit{span-based}. It generates a linearized graph where nodes represent
text spans and edges represent relation triplets. Our method employs a
transformer encoder-decoder architecture with pointing mechanism on a dynamic
vocabulary of spans and relation types. Our model can capture the structural
characteristics and boundaries of entities and relations through span
representations while simultaneously grounding the generated output in the
original text thanks to the pointing mechanism. Evaluation on benchmark
datasets validates the effectiveness of our approach, demonstrating competitive
results. Code is available at this https URL

我们提出了一种新的方法，将无结构文本中的实体和关系提取作为条件序列生成问题，采用基于跨度的方法生成线性化图，其中节点表示文本跨度，边表示关系三元组。通过跨度表示，我们的模型可以捕捉实体和关系的结构特征和边界，并通过指向机制将生成的输出与原始文本进行关联。基准数据集上的评估验证了我们方法的有效性，展示了竞争性的结果。