Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model's generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods, including the data augmentation and prompt-tuning methods. Our codes are available at https://github.com/lifan-yuan/FactMix.
翻译:在有限资源领域对实体进行标记至关重要,因此近年来得到了适当的注意。现有的微小净化方法主要在域内环境下进行评估。相比之下,对于这些内在的忠实模型如何使用几个标签在域内的例子在跨域净化中发挥作用,却知之甚少。本文提出一个两步推论核心数据增强方法,以提高模型的概括能力。若干数据集的结果显示,与以往的先进方法相比,我们的模型-不可知性方法大大改善了跨域净化任务的业绩,包括数据增强和快速调试方法。我们的代码可在https://github.com/lifan-yuan/FactMix查阅。