@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T OasisSimp: An Open-source Asian-English Sentence Simplification Dataset
%A Liu, Hannah
%A Tian, Murphy
%A Ali, Iqra
%A Gao, Haonan
%A Wu, Qiaoyiwen
%A Yang, Blair
%A Thayasivam, Uthayasanker
%A Lee, Annie En-Shiun
%A Nakwijit, Pakawat
%A Ranathunga, Surangika
%A Shekhar, Ravi
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F liu-etal-2026-oasissimp
%X 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.
%R 10.63317/4ws5ja7xyadd
%U https://aclanthology.org/2026.lrec-1.483/
%U https://doi.org/10.63317/4ws5ja7xyadd
%P 6103-6115
Markdown (Informal)
[OasisSimp: An Open-source Asian-English Sentence Simplification Dataset](https://aclanthology.org/2026.lrec-1.483/) (Liu et al., LREC 2026)
ACL
- Hannah Liu, Murphy Tian, Iqra Ali, Haonan Gao, Qiaoyiwen Wu, Blair Yang, Uthayasanker Thayasivam, Annie En-Shiun Lee, Pakawat Nakwijit, Surangika Ranathunga, and Ravi Shekhar. 2026. OasisSimp: An Open-source Asian-English Sentence Simplification Dataset. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6103–6115, Palma de Mallorca, Spain. ELRA Language Resource Association.