RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition

• URL: http://arxiv.org/abs/2307.07417v2
• Date: Mon, 17 Jul 2023 06:08:22 GMT
• Title: RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition
• Authors: Sihan Song, Furao Shen, Jian Zhao
• Abstract summary: Robust Prompt-based Data Augmentation (RoPDA) for low-resource NER Based on pre-trained language models (PLMs) with continuous prompt, RoPDA performs entity augmentation and context augmentation. Experiments on three benchmarks from different domains demonstrate that RoPDA significantly improves upon strong baselines.
• Score: 10.03246698225533
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Data augmentation has been widely used in low-resource NER tasks to tackle the problem of data sparsity. However, previous data augmentation methods have the disadvantages of disrupted syntactic structures, token-label mismatch, and requirement for external knowledge or manual effort. To address these issues, we propose Robust Prompt-based Data Augmentation (RoPDA) for low-resource NER. Based on pre-trained language models (PLMs) with continuous prompt, RoPDA performs entity augmentation and context augmentation through five fundamental augmentation operations to generate label-flipping and label-preserving examples. To optimize the utilization of the augmented samples, we present two techniques: Self-Consistency Filtering and mixup. The former effectively eliminates low-quality samples, while the latter prevents performance degradation arising from the direct utilization of label-flipping samples. Extensive experiments on three benchmarks from different domains demonstrate that RoPDA significantly improves upon strong baselines, and also outperforms state-of-the-art semi-supervised learning methods when unlabeled data is included.

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