Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty
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 ...
| Authors | Yiheng Wu, Jue Hou, Roman Yangarber |
|---|---|
| Primary category | cs.CL |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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