2023/05/22 by Joseph Marvin Imperial, Imperial, Joseph Marvin, Ekaterina Kochmar +1 · 3 citations
Computer Science · Health Professions · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Interpreting and Communication in Healthcare #Natural Language Processing Techniques #Text Readability and Simplification
paper · pdf · doi:10.48550/arxiv.2305.13478
openalex publication_date 2023/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In recent years, the main focus of research on automatic readability assessment (ARA) has shifted towards using expensive deep learning-based methods with the primary goal of increasing models' accuracy. This, however, is rarely applicable for low-resource languages where traditional handcrafted features are still widely used due to the lack of existing NLP tools to extract deeper linguistic representations. In this work, we take a step back from the technical component and focus on how linguistic aspects such as mutual intelligibility or degree of language relatedness can improve ARA in a low-resource setting. We collect short stories written in three languages in the Philippines-Tagalog, Bikol, and Cebuano-to train readability assessment models and explore the interaction of data and features in various cross-lingual setups. Our results show that the inclusion of CrossNGO, a novel specialized feature exploiting n-gram overlap applied to languages with high mutual intelligibility, significantly improves the performance of ARA models compared to the use of off-the-shelf large multilingual language models alone. Consequently, when both linguistic representations are combined, we achieve state-of-the-art results for Tagalog and Cebuano, and baseline scores for ARA in Bikol.