Papers › SiTSE: Sinhala Text Simplification Dataset and Evaluation

SiTSE: Sinhala Text Simplification Dataset and Evaluation

2 Dec 2024arXiv:2412.01293archive 2025-07-28

Surangika Ranathunga, Rumesh Sirithunga, Himashi Rathnayake, Lahiru De Silva, Thamindu Aluthwala, Saman Peramuna, Ravi Shekhar

Text Simplification is a task that has been minimally explored for low-resource languages. Consequently, there are only a few manually curated datasets. In this paper, we present a human curated sentence-level text simplification dataset for the Sinhala language. Our evaluation dataset contains 1,000 complex sentences and corresponding 3,000 simplified sentences produced by three different human annotators. We model the text simplification task as a zero-shot and zero resource sequence-to-sequence (seq-seq) task on the multilingual language models mT5 and mBART. We exploit auxiliary data from related seq-seq tasks and explore the possibility of using intermediate task transfer learning (ITTL). Our analysis shows that ITTL outperforms the previously proposed zero-resource methods for text simplification. Our findings also highlight the challenges in evaluating text simplification systems, and support the calls for improved metrics for measuring the quality of automated text simplification systems that would suit low-resource languages as well. Our code and data are publicly available: https://github.com/brainsharks-fyp17/Sinhala-Text-Simplification-Dataset-and-Evaluation

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Sentence Text Simplification Transfer Learning

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Adafactor Attention Attention Dropout BPE Dense Connections Dropout Gated Linear Unit Inverse Square Root Schedule Layer Normalization Linear Layer Multi-Head Attention Residual Connection SentencePiece Softmax T5 mBART mT5

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