Current research on automatic readability assessment (ARA) has focused on
improving the performance of models in high-resource languages such as English.
In this work, we introduce and release BasahaCorpus as part of an initiative
aimed at expanding available corpora and baseline models for readability
assessment in lower resource languages in the Philippines. We compiled a corpus
of short fictional narratives written in Hiligaynon, Minasbate, Karay-a, and
Rinconada -- languages belonging to the Central Philippine family tree subgroup
-- to train ARA models using surface-level, syllable-pattern, and n-gram
overlap features. We also propose a new hierarchical cross-lingual modeling
approach that takes advantage of a language's placement in the family tree to
increase the amount of available training data. Our study yields encouraging
results that support previous work showcasing the efficacy of cross-lingual
models in low-resource settings, as well as similarities in highly informative
linguistic features for mutually intelligible languages.

在菲律宾的低资源语言中，为了扩大可用的语料库和可读性评估的基线模型，我们介绍和发布了 BasahaCorpus，编制了一组用于训练表层特征、音节模式和 n-gram 重叠特征的 ARA 模型的短篇虚构故事的语料库，还提出了一种新的层次化跨语言建模方法，利用语言在语系树种的位置来增加可用的训练数据。我们的研究取得了令人鼓舞的结果，支持先前工作展示了跨语言模型在低资源环境中的有效性，以及相互可理解语言的高信息语言特征的相似性。