BERT Embeddings for Automatic Readability Assessment
Joseph Marvin Imperial · 2021
Automatic readability assessment (ARA) is the task of evaluating the level of ease or difficulty of text documents for a target audience.For researchers, one of the many open problems in the field is to make such models trained for the task show efficacy even for low-resource languages.In this study, we propose an alternative way of utilizing the information-rich embeddings of BERT models with handcrafted linguistic features through a combined method for readability assessment.Results show that the proposed method outperforms classical approaches in readability assessment using English and Filipino datasets-obtaining as high as 12.4% increase in F1 performance.We also show that the general information encoded in BERT embeddings can be used as a substitute feature set for low-resource languages like Filipino with limited semantic and syntactic NLP tools to explicitly extract feature values for the task.