Template-free Prompt Tuning for Few-shot NER
Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang,, Xuanjing Huang

TL;DR
This paper introduces a template-free prompt tuning approach for few-shot Named Entity Recognition (NER) that reformulates the task as a language modeling problem, eliminating template design and significantly speeding up decoding.
Contribution
The work proposes a novel template-free method for few-shot NER that simplifies the process and improves speed by predicting label words directly without templates.
Findings
Outperforms template-based methods in few-shot NER accuracy
Decoding speed is up to 1930 times faster than template-based approaches
Effective automatic search for appropriate label words enhances model adaptation
Abstract
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 class-related 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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
