@inproceedings{liu-etal-2026-oasissimp,
    title = "{O}asis{S}imp: An Open-source {A}sian-{E}nglish Sentence Simplification Dataset",
    author = "Liu, Hannah  and
      Tian, Murphy  and
      Ali, Iqra  and
      Gao, Haonan  and
      Wu, Qiaoyiwen  and
      Yang, Blair  and
      Thayasivam, Uthayasanker  and
      Lee, Annie En-Shiun  and
      Nakwijit, Pakawat  and
      Ranathunga, Surangika  and
      Shekhar, Ravi",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/test-year-match/2026.lrec-1.483/",
    doi = "10.63317/4ws5ja7xyadd",
    pages = "6103--6115",
    abstract = "Text 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 OasisSimp, a multilingual dataset for sentence-level text 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 through direct human annotation, where trained annotators followed detailed guidelines to simplify sentences while maintaining meaning, fluency, and grammatical correctness. We evaluate eight open-weight multilingual Large Language Models (LLMs) on OasisSimp and observe substantial performance disparities between high-resource and low-resource languages, highlighting the simplification challenges in multilingual settings. OasisSimp 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 text simplification. The dataset will be open-sourced upon acceptance."
}