OasisSimp: An Open-source Asian-English Sentence Simplification Dataset

2026-03-14

AI Summary

This study addresses the critical scarcity of high-quality sentence simplification data for low- and medium-resource languages, which has significantly hindered research on multilingual text accessibility. To bridge this gap, the authors construct the first multilingual sentence simplification dataset encompassing English, Sinhala, Tamil, Pashto, and Thai, curated by professional annotators following a unified annotation protocol that balances semantic preservation and linguistic fluency. Notably, the data for Thai, Pashto, and Tamil are released for the first time, filling key resource voids. Through human evaluation and systematic benchmarking across eight open-source multilingual large language models, the work reveals a pronounced performance disparity between high- and low-resource languages in sentence simplification, thereby establishing a valuable resource and a challenging benchmark for future research.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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/.

Problem

• sentence simplification
• low-resource languages
• multilingual dataset
• linguistic complexity
• data scarcity

Innovation

• sentence simplification
• low-resource languages
• multilingual dataset
• large language models
• OasisSimp