Papers › BERT Embeddings for Automatic Readability Assessment

BERT Embeddings for Automatic Readability Assessment

15 Jun 2021RANLP 2021 9arXiv:2106.07935archive 2025-07-28

Joseph Marvin Imperial

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.

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Code

imperialite/BERT-Embeddings-For-ARA officialmentioned in papermentioned on GitHubMIT report

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Tasks

Text Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Text Classification	OneStopEnglish (Readability Assessment)	Logistic Regression	Accuracy (5-fold)	0.744	#5 of 5	Archive leaderboard	report

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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