Homophone-Based Chinese Natural Language Data Augmentation

Data, devices, and algorithms drive progress in artificial intelligence, and high quality data is essential for model training. However, collecting and labeling such data is costly. Inspired by computer vision, natural language data augmentation has attracted attention as a way to expand corpora through random insertion, deletion, synonym replacement, swapping, and large language model based generation. Most Chinese augmentation methods still follow English style strategies and fail to exploit Chinese specific linguistic properties, which limits their usability and effectiveness. Chinese users often make homophone errors in instant messaging programs such as QQ, WeChat, and WhatsApp. However, readers can still understand the intended meaning from context, background, and language habits. We call this phenomenon homophone consistency. We exploit Chinese homophone consistency for Chinese natural language data augmentation and design a Homophone-based Chinese Augmentation. HCA converts a sentence into a pinyin queue and then converts the pinyin queue back into a sentence using a dictionary based dynamic programming algorithm for pinyin identification. Words in the original sentence are replaced with homophones with a specified probability. HCA broadens Chinese augmentation using Chinese features, enriches the Chinese data augmentation strategy space, and provides a reference for designing language specific augmentation methods for other languages. We fine tune Chinese BERT on four datasets of different sizes and evaluate test accuracy. HCA improves test accuracy by up to 37.62% when the dataset is small and improves it by 3.42% on average. We also conduct ablation studies to analyze the effects of key parameters on training outcomes.