  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.

Computation and Language