Pre-training with Meta Learning for Chinese Word Segmentation

• URL: http://arxiv.org/abs/2010.12272v2
• Date: Mon, 15 Mar 2021 07:15:20 GMT
• Title: Pre-training with Meta Learning for Chinese Word Segmentation
• Authors: Zhen Ke, Liang Shi, Songtao Sun, Erli Meng, Bin Wang, Xipeng Qiu
• Abstract summary: We propose a CWS-specific pre-trained model METASEG, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task. METASEG can achieve new state-of-the-art performance on twelve widely-used CWS datasets.
• Score: 44.872788258481755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between pre-training tasks and downstream CWS tasks. In this paper, we propose a CWS-specific pre-trained model METASEG, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task. Empirical results show that METASEG could utilize common prior segmentation knowledge from different existing criteria and alleviate the discrepancy between pre-trained models and downstream CWS tasks. Besides, METASEG can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.

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