Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks

• URL: http://arxiv.org/abs/2202.13840v1
• Date: Mon, 28 Feb 2022 14:54:08 GMT
• Title: Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
• Authors: Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, Zhongyuan Wang, Songlin Hu
• Abstract summary: Smoothed representation is the probability of candidate tokens obtained from a pre-trained masked language model. We propose an efficient data augmentation method, termed text smoothing, by converting a sentence from its one-hot representation to a controllable smoothed representation.
• Score: 47.5423959822716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary. Smoothed representation is the probability of candidate tokens obtained from a pre-trained masked language model, which can be seen as a more informative substitution to the one-hot representation. We propose an efficient data augmentation method, termed text smoothing, by converting a sentence from its one-hot representation to a controllable smoothed representation. We evaluate text smoothing on different benchmarks in a low-resource regime. Experimental results show that text smoothing outperforms various mainstream data augmentation methods by a substantial margin. Moreover, text smoothing can be combined with those data augmentation methods to achieve better performance.

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