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。

具有关系关注和类型关注的多任务 Transformer 用于命名实体识别

Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition

Named entity recognition is a traditional task in natural language
processing. In particular, nested entity recognition receives extensive
attention for the widespread existence of the nesting scenario. The latest
research migrates the well-established paradigm of set prediction in object
detection to cope with entity nesting. However, the manual creation of query
vectors, which fail to adapt to the rich semantic information in the context,
limits these approaches. An end-to-end entity detection approach with proposer
and regressor is presented in this paper to tackle the issues. First, the
proposer utilizes the feature pyramid network to generate high-quality entity
proposals. Then, the regressor refines the proposals for generating the final
prediction. The model adopts encoder-only architecture and thus obtains the
advantages of the richness of query semantics, high precision of entity
localization, and easiness of model training. Moreover, we introduce the novel
spatially modulated attention and progressive refinement for further
improvement. Extensive experiments demonstrate that our model achieves advanced
performance in flat and nested NER, achieving a new state-of-the-art F1 score
of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset.

本文提出了一个基于 proposer 和 regressor 的端到端实体检测方法，采用编码器架构，使用空间调制注意力和渐进细化方法来进一步提高性能，模型在平面和嵌套类型的命名实体识别任务中均获得了先进性能，在 GENIA 数据集上达到了 80.74 的 F1 分数，在 WeiboNER 数据集上达到了 72.38。