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Sentence Simplification is a valuable technique that can benefit languagelearners and children a lot. However, current research focuses more on Englishsentence simplification. The development of Chinese sentence simplification isrelatively slow due to the lack of data. To alleviate this limitation, thispaper introduces CSS, a new dataset for assessing sentence simplification inChinese. We collect manual simplifications from human annotators and performdata analysis to show the difference between English and Chinese sentencesimplifications. Furthermore, we test several unsupervised and zero/few-shotlearning methods on CSS and analyze the automatic evaluation and humanevaluation results. In the end, we explore whether Large Language Models canserve as high-quality Chinese sentence simplification systems by evaluatingthem on CSS.

A New Dataset and Empirical Study for Sentence Simplification in Chinese