OasisSimp: An Open-source Asian-English Sentence Simplification Dataset
This provides a valuable multilingual resource and benchmark for low-resource sentence simplification, though it is incremental as it builds on existing data efforts.
The authors tackled the scarcity of high-quality sentence simplification data for mid- and low-resource languages by introducing the OasisSimp dataset covering English, Sinhala, Tamil, Pashto, and Thai, and found substantial performance disparities between high- and low-resource languages when evaluating multilingual LLMs.
Sentence simplification aims to make complex text more accessible by reducing linguistic complexity while preserving the original meaning. However, progress in this area remains limited for mid-resource and low-resource languages due to the scarcity of high-quality data. To address this gap, we introduce the OasisSimp dataset, a multilingual dataset for sentence-level simplification covering five languages: English, Sinhala, Tamil, Pashto, and Thai. Among these, no prior sentence simplification datasets exist for Thai, Pashto, and Tamil, while limited data is available for Sinhala. Each language simplification dataset was created by trained annotators who followed detailed guidelines to simplify sentences while maintaining meaning, fluency, and grammatical correctness. We evaluate eight open-weight multilingual Large Language Models (LLMs) on the OasisSimp dataset and observe substantial performance disparities between high-resource and low-resource languages, highlighting the simplification challenges in multilingual settings. The OasisSimp dataset thus provides both a valuable multilingual resource and a challenging benchmark, revealing the limitations of current LLM-based simplification methods and paving the way for future research in low-resource sentence simplification. The dataset is available at https://OasisSimpDataset.github.io/.