“Chinese sentence simplification faces challenges due to the lack of large-scale labeledparallel corpora and the prevalence of idioms. To address these challenges, we pro-pose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel frameworkthat combines data augmentation techniques. RISS introduces two key components: (1)Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sen-tence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the compre-hension and simplification of idiomatic expressions. By integrating RPS and IAS usingmulti-stage and multi-task learning strategies, RISS outperforms previous state-of-the-artmethods on two Chinese sentence simplification datasets. Furthermore, RISS achievesadditional improvements when fine-tuned on a small labeled dataset. Our approachdemonstrates the potential for more effective and accessible Chinese text simplification.”

“This system report presents our approaches and results for the Chinese Essay Fluency Evaluation (CEFE) task at CCL-2024. For Track 1, we optimized predictions for challenging fine-grained error types using binary classification models and trained coarse-grained models on the Chinese Learner 4W corpus. In Track 2, we enhanced performance by constructing a pseudo-dataset with multiple error types per sentence. For Track 3, where we achieved first place, we generated fluency-rated pseudo-data via back-translation for pretraining and used an NSP-based strategy with Symmetric Cross Entropy loss to capture context and mitigate long dependencies. Our methods effectively address key challenges in Chinese Essay Fluency Evaluation.”