BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages
This work addresses the problem of limited linguistic resources for readability assessment in specific low-resource languages, which is incremental as it builds on existing cross-lingual methods.
The authors tackled the lack of resources for automatic readability assessment in low-resource Central Philippine languages by introducing BasahaCorpus, a compiled dataset of short fictional narratives in four languages, and achieved encouraging results with a hierarchical cross-lingual modeling approach that leverages language family relationships.
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.