A New Dataset and Empirical Study for Sentence Simplification in Chinese
Shiping Yang Renliang Sun Xiaojun Wan
Sentence Simplification is a valuable technique that can benefit language
learners and children a lot. However, current research focuses more on English
sentence simplification. The development of Chinese sentence simplification is
relatively slow due to the lack of data. To alleviate this limitation, this
paper introduces CSS, a new dataset for assessing sentence simplification in
Chinese. We collect manual simplifications from human annotators and perform
data analysis to show the difference between English and Chinese sentence
simplifications. Furthermore, we test several unsupervised and zero/few-shot
learning methods on CSS and analyze the automatic evaluation and human
evaluation results. In the end, we explore whether Large Language Models can
serve as high-quality Chinese sentence simplification systems by evaluating
them on CSS.