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.

本文提出一种多任务 Transformer 模型，该模型将实体边界检测任务整合到命名实体识别任务中，通过关系分类来实现实体边界检测，并采用外部知识库和自注意力及交叉注意力机制来提高解码时实体类型映射的准确性。在多个实验数据集上的实验结果表明，该方法显著提高了生成型 NER 模型的性能。