How to choose "Good" Samples for Text Data Augmentation
This addresses the challenge of selecting reliable augmented data for text classification, which is incremental as it builds on existing data augmentation methods by adding a selection step.
The paper tackles the problem of noisy augmented samples in text data augmentation by proposing a self-training selection framework with two selectors to filter high-quality samples, resulting in improved performance as shown in experimental results.
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.