  There is a recent interest in investigating few-shot NER, where the
low-resource target domain has different label sets compared with a
resource-rich source domain. Existing methods use a similarity-based metric.
However, they cannot make full use of knowledge transfer in NER model
parameters. To address the issue, we propose a template-based method for NER,
treating NER as a language model ranking problem in a sequence-to-sequence
framework, where original sentences and statement templates filled by candidate
named entity span are regarded as the source sequence and the target sequence,
respectively. For inference, the model is required to classify each candidate
span based on the corresponding template scores. Our experiments demonstrate
that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource
task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and
11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS
(low-resource task), respectively.
