Mask-guided BERT for Few Shot Text Classification

• URL: http://arxiv.org/abs/2302.10447v1
• Date: Tue, 21 Feb 2023 05:24:00 GMT
• Title: Mask-guided BERT for Few Shot Text Classification
• Authors: Wenxiong Liao, Zhengliang Liu, Haixing Dai, Zihao Wu, Yiyang Zhang, Xiaoke Huang, Yuzhong Chen, Xi Jiang, Dajiang Zhu, Tianming Liu, Sheng Li, Xiang Li, Hongmin Cai
• Abstract summary: Mask-BERT is a simple and modular framework to help BERT-based architectures tackle few-shot learning. The core idea is to selectively apply masks on text inputs and filter out irrelevant information, which guides the model to focus on discriminative tokens. Experimental results on public-domain benchmark datasets demonstrate the effectiveness of Mask-BERT.
• Score: 12.361032727044547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting. Here we present Mask-BERT, a simple and modular framework to help BERT-based architectures tackle FSL. The proposed approach fundamentally differs from existing FSL strategies such as prompt tuning and meta-learning. The core idea is to selectively apply masks on text inputs and filter out irrelevant information, which guides the model to focus on discriminative tokens that influence prediction results. In addition, to make the text representations from different categories more separable and the text representations from the same category more compact, we introduce a contrastive learning loss function. Experimental results on public-domain benchmark datasets demonstrate the effectiveness of Mask-BERT.

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