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.

翻译：句子简化是一项对语言学习者和儿童大有裨益的技术。然而，当前研究主要聚焦于英文句子简化，中文句子简化的进展因数据匮乏而相对缓慢。为缓解这一局限，本文引入了CSS（中文句子简化评估数据集），这是一个专为评估中文句子简化任务而构建的新数据集。我们收集了人工标注者的简化结果，并通过数据分析揭示了中英文句子简化之间的差异。此外，我们在CSS上测试了多种无监督及零样本/少样本学习方法，并对自动评估与人工评估结果进行了分析。最后，通过大语言模型在CSS上的表现，我们探讨了其能否作为高质量中文句子简化系统的可能性。