We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, which uniformly disassembles all extraction targets into joint span detection, classification and association problems with a unified extractive framework, namely UniEX. UniEX can synchronously encode schema-based prompt and textual information, and collaboratively learn the generalized knowledge from pre-defined information using the auto-encoder language models. We develop a traffine attention mechanism to integrate heterogeneous factors including tasks, labels and inside tokens, and obtain the extraction target via a scoring matrix. Experiment results show that UniEX can outperform generative universal IE models in terms of performance and inference-speed on $14$ benchmarks IE datasets with the supervised setting. The state-of-the-art performance in low-resource scenarios also verifies the transferability and effectiveness of UniEX.

翻译：我们提出了一种适用于任意模式格式且可应用于多项信息提取（IE）任务的通用范式，包括命名实体识别、关系抽取、事件抽取和情感分析。本方法将基于文本的信息提取任务转化为令牌对问题，通过统一提取框架UniEX将各类提取目标拆解为跨度检测、分类与关联的联合问题。UniEX能够同步编码基于模式的提示信息和文本信息，并利用自编码语言模型从预定义信息中协同学习通用知识。我们设计了交通注意力机制以整合任务、标签及内部令牌等异构因素，并通过评分矩阵获取提取目标。实验结果表明，在14个有监督基准IE数据集上，UniEX在性能和推理速度方面均优于生成式通用IE模型。低资源场景下的最优性能也验证了UniEX的迁移性和有效性。