Template free Prompt Tuning for Few shot NER
Prompt based methods have been successfully applied in sentence level few shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token level labeling tasks such as NER, it would be time consuming to enumerate the template queries over all potential entity spans. In this work, we propose a more elegant method to reformulate NER tasks as LM problems without any templates. Specifically, we discard the template construction process while maintaining the word prediction paradigm of pre training models to predict a classrelated pivot word (or label word) at the entity position. Meanwhile, we also explore principled ways to automatically search for appropriate label words that the pre trained models can easily adapt to. While avoiding complicated template based process, the proposed LM objective also reduces the gap between different objectives used in pretraining and fine tuning, thus it can better benefit the fewshot performance. Experimental results demonstrate the effectiveness of the proposed method over bert tagger and template based method under few shot setting. Moreover, the decoding speed of the proposed method is up to 1930.12 times faster than the template based method.
ArXiv