Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty

• URL: http://arxiv.org/abs/2607.19101v1
• Date: Tue, 21 Jul 2026 13:43:46 GMT
• Title: Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty
• Abstract summary: Machine translation is used to transfer labeled resources from high-resource languages to low-resource languages.<n>We train BERT-based regression models to predict difficulty scores and investigate whether synthetic, translated data can effectively supplement native training sets.<n>Our experiments demonstrate that augmenting scarce native data with machine-translated corpora significantly improves the accuracy of difficulty estimation.
• Score: 4.72358375472406
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications. However, the development of robust assessment models is severely hindered by a critical bottleneck: the scarcity of expert-annotated corpora containing fine-grained difficulty levels (e.g., CEFR), particularly for lower-resource languages. This paper addresses this data scarcity problem in the context of a low-resource European language. We propose a cross-lingual data augmentation strategy that leverages machine translation to transfer labeled resources from high-resource languages to the target low-resource language. We train BERT-based regression models to predict difficulty scores and investigate whether synthetic, translated data can effectively supplement native training sets. Our experiments demonstrate that augmenting scarce native data with machine-translated corpora significantly improves the accuracy of difficulty estimation, offering a viable solution for languages lacking extensive expert annotations.

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