Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling

• URL: http://arxiv.org/abs/2307.07946v2
• Date: Thu, 20 Jul 2023 02:01:34 GMT
• Title: Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
• Authors: Zifeng Cheng, Qingyu Zhou, Zhiwei Jiang, Xuemin Zhao, Yunbo Cao, Qing Gu
• Abstract summary: Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. We propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. Our model achieves new state-of-the-art results on three benchmark datasets.
• Score: 18.24907067631541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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