Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques with lexcial simplification. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification.

通过结合数据增强技术和词汇简化，我们提出了一种名为RISS（Readability-guided Idiom-aware Sentence Simplification）的新框架，用于解决中文句子简化面临的挑战，包括缺乏大规模标记平行语料库和惯用语的普遍存在。RISS通过多阶段和多任务学习策略将RPS和IAS整合在一起，从而在两个中文句子简化数据集上优于先前的最先进方法。此外，在小型标记数据集上微调后，RISS还取得了进一步的改进。我们的方法展示了更加有效和可访问的中文文本简化的潜力。

基于可读性引导的成语感知句子简化（RISS）对中文的应用