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

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.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2607.19101 [cs.CL]

(or arXiv:2607.19101v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.19101 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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