Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token level or span level) and have some weaknesses of the corresponding granularity. In this paper, we first unify token and span level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token-level and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets.

翻译：少样本序列标注旨在仅基于少量标注样本识别新类别。现有方法主要通过设计基于度量学习的令牌级或跨度级标注模型来解决数据稀缺问题。然而，这些方法仅在单一粒度（即令牌级或跨度级）上训练，并存在相应粒度的某些弱点。本文首次统一令牌与跨度监督，提出一种用于少样本序列标注的一致性双自适应原型（CDAP）网络。CDAP包含令牌级网络和跨度级网络，在不同粒度上联合训练。为对齐两个网络的输出，我们进一步提出一致性损失函数，使它们能够相互学习。在推理阶段，我们提出一种一致性贪心推理算法，该算法首先调整预测概率，然后贪婪地选取最大概率的非重叠跨度。大量实验表明，我们的模型在三个基准数据集上取得了新的最佳结果。