Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play to the differentiation advantages of different models, especially in the open domain. This paper describes our NER system in the SemEval 2022 task11: MultiCoNER. We proposed an effective system to adaptively ensemble pre-trained language models by a Transformer layer. By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effectively. Experimental results show that our method achieves superior performances in Farsi and Dutch.

翻译：大型培训前模式被广泛用于命名实体识别(NER)任务,然而,通过平均参数或投票实现的模型组合无法充分体现不同模式的差别优势,特别是在开放领域。本文描述了我们在SemEval 2022任务11中的NER系统:多合作网络。我们提出了一个有效的系统,通过一个变异层,通过适应性地组合预先培训的语言模式。我们为不同的投入给每个模型分配不同的权重,从而采用了变异层,有效地整合了不同模型的优势。实验结果表明,我们的方法在法西和荷兰取得了优异的成绩。