BERT Embeddings for Automatic Readability Assessment

TL;DR

This paper proposes a combined BERT embedding and handcrafted feature approach for automatic readability assessment, demonstrating improved performance in English and Filipino, and showing potential for low-resource languages.

Contribution

It introduces a novel method combining BERT embeddings with linguistic features, enhancing readability assessment especially for low-resource languages.

Findings

Outperforms classical methods with up to 12.4% F1 increase in English and Filipino.

BERT embeddings can substitute explicit feature extraction in low-resource languages.

Method shows effectiveness across languages with limited NLP tools.

Abstract

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.