Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese
This addresses the problem of making Chinese text more accessible, particularly for language learners or those with reading difficulties, by improving simplification accuracy, though it appears incremental as it builds on existing methods.
The paper tackled the challenges of Chinese sentence simplification due to limited labeled data and idioms by proposing the RISS framework, which outperformed previous state-of-the-art methods on two datasets and showed further gains with fine-tuning.
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.