SiTSE: Sinhala Text Simplification Dataset and Evaluation
This work addresses the problem of text simplification for low-resource languages like Sinhala, though it is incremental as it builds on existing multilingual models and methods.
The authors tackled the lack of text simplification resources for low-resource languages by creating a human-curated dataset of 1,000 complex and 3,000 simplified sentences in Sinhala, and found that intermediate task transfer learning outperforms previous zero-resource methods.
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