mlsanityData to 14 Oct 2026

A New Dataset and Empirical Study for Sentence Simplification in Chinese

arXiv 2306.04188 ↗First posted 7 Jun 2023cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

22 of 53 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025Preference Leakage: A Contamination Problem in LLM-as-a-judgecited by 125
  2. 2024mEdIT: Multilingual Text Editing via Instruction Tuningcited by 13
  3. 2025Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Datacited by 10
  4. 2024Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinesecited by 1