How to choose "Good" Samples for Text Data Augmentation

• URL: http://arxiv.org/abs/2302.00894v1
• Date: Thu, 2 Feb 2023 06:01:50 GMT
• Title: How to choose "Good" Samples for Text Data Augmentation
• Authors: Xiaotian Lin, Nankai Lin, Yingwen Fu, Ziyu Yang and Shengyi Jiang
• Abstract summary: We propose a novel self-training framework with two selectors to select high-quality samples from data augmentation. Specifically, we firstly use an entropy-based strategy and the model prediction to select augmented samples. Considering some samples with high quality at the above step may be wrongly filtered, we propose to recall them from two perspectives of word overlap and semantic similarity.
• Score: 4.60495447017298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation to expand the corpus size. However, data augmentation may potentially produce some noisy augmented samples. There are currently no works exploring sample selection for augmented samples in nature language processing field. In this paper, we propose a novel self-training selection framework with two selectors to select the high-quality samples from data augmentation. Specifically, we firstly use an entropy-based strategy and the model prediction to select augmented samples. Considering some samples with high quality at the above step may be wrongly filtered, we propose to recall them from two perspectives of word overlap and semantic similarity. Experimental results show the effectiveness and simplicity of our framework.

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