# A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss

arXiv: https://arxiv.org/abs/2210.10305 · HTML: https://mlsanity.com/paper/2210.10305/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [Supervised and Unsupervised Neural Approaches to Text Readability](https://mlsanity.com/paper/1907.11779/) (2019)
- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [Linguistic Features for Readability Assessment](https://mlsanity.com/paper/2006.00377/) (2020)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge](https://mlsanity.com/paper/1611.10277/) (2016)
- [ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis](https://mlsanity.com/paper/2103.04083/) (2021)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)

## Built on by (most cited first, 7 of 7)

- [LongWanjuan: Towards Systematic Measurement for Long Text Quality](https://mlsanity.com/paper/2402.13583/) (2024)
- [Rank-Then-Score: Enhancing Large Language Models for Automated Essay Scoring](https://mlsanity.com/paper/2504.05736/) (2025)
- [Representation Purification for End-to-End Speech Translation](https://mlsanity.com/paper/2412.04266/) (2024)
- [Zero-shot Large Language Models for Automatic Readability Assessment](https://mlsanity.com/paper/2604.24470/) (2026)
- [FPT: Feature Prompt Tuning for Few-shot Readability Assessment](https://mlsanity.com/paper/2404.02772/) (2024)
- [Multi-Facet Counterfactual Learning for Content Quality Evaluation](https://mlsanity.com/paper/2410.07693/) (2024)
- [Hierarchical Ranking Neural Network for Long Document Readability Assessment](https://mlsanity.com/paper/2511.21473/) (2025)

## Abstract (arXiv)

For readability assessment, traditional methods mainly employ machine learning classifiers with hundreds of linguistic features. Although the deep learning model has become the prominent approach for almost all NLP tasks, it is less explored for readability assessment. In this paper, we propose a BERT-based model with feature projection and length-balanced loss (BERT-FP-LBL) for readability assessment. Specially, we present a new difficulty knowledge guided semi-supervised method to extract topic features to complement the traditional linguistic features. From the linguistic features, we employ projection filtering to extract orthogonal features to supplement BERT representations. Furthermore, we design a new length-balanced loss to handle the greatly varying length distribution of data. Our model achieves state-of-the-art performances on two English benchmark datasets and one dataset of Chinese textbooks, and also achieves the near-perfect accuracy of 99% on one English dataset. Moreover, our proposed model obtains comparable results with human experts in consistency test.