Named entity recognition (NER) is an important research problem in natural language processing. There are three types of NER tasks, including flat, nested and discontinuous entity recognition. Most previous sequential labeling models are task-specific, while recent years have witnessed the rising of generative models due to the advantage of unifying all NER tasks into the seq2seq model framework. Although achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. This paper proposes a multi-task Transformer, which incorporates an entity boundary detection task into the named entity recognition task. More concretely, we achieve entity boundary detection by classifying the relations between tokens within the sentence. To improve the accuracy of entity-type mapping during decoding, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information into the model via the self and cross-attention mechanisms. We perform experiments on an extensive set of NER benchmarks, including two flat, three nested, and three discontinuous NER datasets. Experimental results show that our approach considerably improves the generative NER model's performance.

翻译：命名实体识别（NER）是自然语言处理中的一个重要研究问题。NER任务分为三种类型：平面实体识别、嵌套实体识别和不连续实体识别。以往的序列标注模型大多针对特定任务，而近年来，由于将各类NER任务统一到序列到序列（seq2seq）模型框架中的优势，生成式模型逐渐兴起。虽然这些模型取得了令人鼓舞的性能，但我们的初步研究表明，现有生成式模型在检测实体边界和估计实体类型方面存在不足。本文提出了一种多任务Transformer，将实体边界检测任务融入命名实体识别任务中。具体地，我们通过分类句子中标记（token）间的关系来实现实体边界检测。为提升解码过程中实体类型映射的准确性，我们采用外部知识库计算先验实体类型分布，并通过自注意力机制和交叉注意力机制将其信息整合到模型中。我们在广泛的NER基准数据集上进行了实验，包括两个平面、三个嵌套和三个不连续NER数据集。实验结果表明，我们的方法显著提升了生成式NER模型的性能。