@inproceedings{wu-etal-2026-translation,
    title = "Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty",
    author = "Wu, Yiheng  and
      Hou, Jue  and
      Yangarber, Roman",
    editor = "Di Nunzio, Giorgio Maria  and
      Vezzani, Federica  and
      Ermakova, Liana  and
      Azarbonyad, Hosein  and
      Kamps, Jaap",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context ({D}e{T}erm{I}t! 2026)",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/backfill-openreview/2026.determit-1.5/",
    doi = "10.63317/3mmqujpgyua5",
    pages = "42--50",
    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."
}