In recent years, the fine-tuned generative models have been proven more
powerful than the previous tagging-based or span-based models on named entity
recognition (NER) task. It has also been found that the information related to
entities, such as entity types, can prompt a model to achieve NER better.
However, it is not easy to determine the entity types indeed existing in the
given sentence in advance, and inputting too many potential entity types would
distract the model inevitably. To exploit entity types' merit on promoting NER
task, in this paper we propose a novel NER framework, namely ToNER based on a
generative model. In ToNER, a type matching model is proposed at first to
identify the entity types most likely to appear in the sentence. Then, we
append a multiple binary classification task to fine-tune the generative
model's encoder, so as to generate the refined representation of the input
sentence. Moreover, we add an auxiliary task for the model to discover the
entity types which further fine-tunes the model to output more accurate
results. Our extensive experiments on some NER benchmarks verify the
effectiveness of our proposed strategies in ToNER that are oriented towards
entity types' exploitation.

通过提出一种新颖的 NER 框架，名为 ToNER，基于生成模型，利用实体类型的优势促进 NER 任务，并通过在生成模型的编码器上追加多个二分类任务、增加辅助任务和进行广泛实验验证，证明了我们所提出的面向实体类型的 ToNER 策略的有效性。