Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition
Zihan Wang, Kewen Zhao, Zilong Wang, Jingbo Shang · 2022
Fine-tuning pre-trained language models is a common practice in building NLP models for various tasks, including the case with less supervision.We argue that under the few-shot setting, formulating fine-tuning closer to the pretraining objective shall be able to unleash more benefits from the pre-trained language models.In this work, we take few-shot named entity recognition (NER) for a pilot study, where existing fine-tuning strategies are much different from pre-training.We propose a novel few-shot fine-tuning framework for NER, FFF-NER.Specifically, we introduce three new types of tokens, "is-entity", "which-type" and bracket, so we can formulate the NER fine-tuning as (masked) token prediction or generation, depending on the choice of the pre-training objective.In our experiments, we apply FFF-NER to fine-tune both BERT and BART for fewshot NER on several benchmark datasets and observe significant improvements over existing fine-tuning strategies, including sequence labeling, prototype meta-learning, and promptbased approaches.We further perform a series of ablation studies, showing few-shot NER performance is strongly correlated with the similarity between fine-tuning and pre-training.