Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features
This work addresses text readability assessment for applications like education and content creation, but it is incremental as it combines existing methods rather than introducing a fundamentally new approach.
The paper tackles text readability assessment by combining transformer models with handcrafted linguistic features, achieving state-of-the-art accuracy of 99% on popular datasets, which is a 20.3% improvement over previous methods.
We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.