OasisSimp：一个开源的亚洲-英语句子简化数据集
OasisSimp: An Open-source Asian-English Sentence Simplification Dataset
AI总结：
本文提出OasisSimp数据集，涵盖五种语言的句子简化任务，揭示了多语言环境下低资源语言的简化挑战，为未来研究提供基准。
摘要：
句子简化旨在通过减少语言复杂性使文本更易懂，同时保持原意。然而，中低资源语言在该领域的进展有限，因高质量数据稀缺。为填补这一空白，我们引入OasisSimp数据集，涵盖五种语言：英语、僧伽罗语、泰米尔语、普什图语和泰语。其中，泰语、普什图语和泰米尔语此前无现有句子简化数据集，僧伽罗语数据有限。每个语言的简化数据集由受训标注者根据详细指南创建，以确保句子简化的同时保持意义、流畅性和语法正确性。我们评估了八种开源多语言大语言模型（LLMs）在OasisSimp数据集上的表现，发现高资源语言与低资源语言之间存在显著性能差异，突显了多语言环境下的简化挑战。OasisSimp数据集因此提供了一个有价值的多语言资源和挑战性的基准，揭示了当前基于LLM的简化方法的局限性，并为未来低资源句子简化研究铺平道路。数据集可在https://OasisSimpDataset.github.io/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
