We present a novel approach to named entity recognition (NER) in the presence
of scarce data that we call example-based NER. Our train-free few-shot learning
approach takes inspiration from question-answering to identify entity spans in
a new and unseen domain. In comparison with the current state-of-the-art, the
proposed method performs significantly better, especially when using a low
number of support examples.

我们提出了一种新的名词实体识别方法，称为基于示例的实体识别，在缺乏数据的情况下通过受到问答启发，使用少量支持示例来识别新领域中的实体范围，与当前最先进的方法相比，该方法表现出更好的性能，特别是在使用少量支持示例时。

基于示例的命名实体识别

Example-Based Named Entity Recognition

Feature attribution methods, proposed recently, help users interpret the
predictions of complex models. Our approach integrates feature attributions
into the objective function to allow machine learning practitioners to
incorporate priors in model building. To demonstrate the effectiveness our
technique, we apply it to two tasks: (1) mitigating unintended bias in text
classifiers by neutralizing identity terms; (2) improving classifier
performance in a scarce data setting by forcing the model to focus on toxic
terms. Our approach adds an L2 distance loss between feature attributions and
task-specific prior values to the objective. Our experiments show that i) a
classifier trained with our technique reduces undesired model biases without a
trade off on the original task; ii) incorporating priors helps model
performance in scarce data settings.

该论文提出了一种将特征规定融入机器学习模型建立的目标函数的方法，以解决文本分类中的偏见和数据稀缺性问题，并通过实验证明其有效性。