Few-shot named entity recognition (NER) systems recognize entities using a
few labeled training examples. The general pipeline consists of a span detector
to identify entity spans in text and an entity-type classifier to assign types
to entities. Current span detectors rely on extensive manual labeling to guide
training. Almost every span detector requires initial training on basic span
features followed by adaptation to task-specific features. This process leads
to repetitive training of the basic span features among span detectors.
Additionally, metric-based entity-type classifiers, such as prototypical
networks, typically employ a specific metric that gauges the distance between
the query sample and entity-type referents, ultimately assigning the most
probable entity type to the query sample. However, these classifiers encounter
the sample dependency problem, primarily stemming from the limited samples
available for each entity-type referent. To address these challenges, we
proposed an improved few-shot NER pipeline. First, we introduce a steppingstone
span detector that is pre-trained on open-domain Wikipedia data. It can be used
to initialize the pipeline span detector to reduce the repetitive training of
basic features. Second, we leverage a large language model (LLM) to set
reliable entity-type referents, eliminating reliance on few-shot samples of
each type. Our model exhibits superior performance with fewer training steps
and human-labeled data compared with baselines, as demonstrated through
extensive experiments on various datasets. Particularly in fine-grained
few-shot NER settings, our model outperforms strong baselines, including
ChatGPT. We will publicly release the code, datasets, LLM outputs, and model
checkpoints.

我们的研究提出了一个改进的少样本命名实体识别流程，利用预训练的跨领域跳板标识检测器和大规模语言模型，有效减少了基本特征的重复训练，同时消除了对少样本的依赖，通过广泛的实验表明，在各种数据集上，我们的模型在细粒度少样本命名实体识别方面胜过基线模型，包括 ChatGPT。

应对 Few-shot 命名实体识别中的重复训练和样本依赖问题的研究

Fighting Against the Repetitive Training and Sample Dependency Problem  in Few-shot Named Entity Recognition

This paper presents an extension of the Stochastic Answer Network (SAN), one
of the state-of-the-art machine reading comprehension models, to be able to
judge whether a question is unanswerable or not. The extended SAN contains two
components: a span detector and a binary classifier for judging whether the
question is unanswerable, and both components are jointly optimized.
Experiments show that SAN achieves the results competitive to the
state-of-the-art on Stanford Question Answering Dataset (SQuAD) 2.0. To
facilitate the research on this field, we release our code:
this https URL

该论文介绍了一种扩展的 Stochastic Answer Network 模型，添加了一个判断问题是否无法回答的二元分类器和句子定位模块，并通过实验证明其在 Stanford Question Answering Dataset (SQuAD) 2.0 数据集上具有与最先进模型相当的效果。