RoChBert: Towards Robust BERT Fine-tuning for Chinese

• URL: http://arxiv.org/abs/2210.15944v1
• Date: Fri, 28 Oct 2022 07:08:00 GMT
• Title: RoChBert: Towards Robust BERT Fine-tuning for Chinese
• Authors: Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun and Chao Zhang
• Abstract summary: RoChBERT is a framework to build more Robust BERT-based models. It fuses Chinese phonetic and glyph features into pre-trained representations during fine-tuning.
• Score: 31.573147796706223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite of the superb performance on a wide range of tasks, pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts. In this paper, we present RoChBERT, a framework to build more Robust BERT-based models by utilizing a more comprehensive adversarial graph to fuse Chinese phonetic and glyph features into pre-trained representations during fine-tuning. Inspired by curriculum learning, we further propose to augment the training dataset with adversarial texts in combination with intermediate samples. Extensive experiments demonstrate that RoChBERT outperforms previous methods in significant ways: (i) robust -- RoChBERT greatly improves the model robustness without sacrificing accuracy on benign texts. Specifically, the defense lowers the success rates of unlimited and limited attacks by 59.43% and 39.33% respectively, while remaining accuracy of 93.30%; (ii) flexible -- RoChBERT can easily extend to various language models to solve different downstream tasks with excellent performance; and (iii) efficient -- RoChBERT can be directly applied to the fine-tuning stage without pre-training language model from scratch, and the proposed data augmentation method is also low-cost.

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