# Automatic Readability Assessment for Closely Related Languages

arXiv: https://arxiv.org/abs/2305.13478 · HTML: https://mlsanity.com/paper/2305.13478/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [Linguistic Features for Readability Assessment](https://mlsanity.com/paper/2006.00377/) (2020)
- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)
- [Experiments with Universal CEFR Classification](https://mlsanity.com/paper/1804.06636/) (2018)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [A Baseline Readability Model for Cebuano](https://mlsanity.com/paper/2203.17225/) (2022)
- [The impact of lexical and grammatical processing on generating code from natural language](https://mlsanity.com/paper/2202.13972/) (2022)
- [A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity](https://mlsanity.com/paper/1603.08868/) (2016)
- [Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts](https://mlsanity.com/paper/2108.00241/) (2021)
- [Under the Microscope: Interpreting Readability Assessment Models for Filipino](https://mlsanity.com/paper/2110.00157/) (2021)

## Built on by (most cited first, 9 of 9)

- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://mlsanity.com/paper/2406.03030/) (2024)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [FilBench: Can LLMs Understand and Generate Filipino?](https://mlsanity.com/paper/2508.03523/) (2025)
- [CebuaNER: A New Baseline Cebuano Named Entity Recognition Model](https://mlsanity.com/paper/2310.00679/) (2023)
- [BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages](https://mlsanity.com/paper/2310.11584/) (2023)
- [Strategies for Arabic Readability Modeling](https://mlsanity.com/paper/2407.03032/) (2024)
- [Lexicon-Enriched Graph Modeling for Arabic Document Readability Prediction](https://mlsanity.com/paper/2509.22870/) (2025)
- [HiligayNER: A Baseline Named Entity Recognition Model for Hiligaynon](https://mlsanity.com/paper/2510.10776/) (2025)

## Abstract (arXiv)

In recent years, the main focus of research on automatic readability assessment (ARA) has shifted towards using expensive deep learning-based methods with the primary goal of increasing models' accuracy. This, however, is rarely applicable for low-resource languages where traditional handcrafted features are still widely used due to the lack of existing NLP tools to extract deeper linguistic representations. In this work, we take a step back from the technical component and focus on how linguistic aspects such as mutual intelligibility or degree of language relatedness can improve ARA in a low-resource setting. We collect short stories written in three languages in the Philippines-Tagalog, Bikol, and Cebuano-to train readability assessment models and explore the interaction of data and features in various cross-lingual setups. Our results show that the inclusion of CrossNGO, a novel specialized feature exploiting n-gram overlap applied to languages with high mutual intelligibility, significantly improves the performance of ARA models compared to the use of off-the-shelf large multilingual language models alone. Consequently, when both linguistic representations are combined, we achieve state-of-the-art results for Tagalog and Cebuano, and baseline scores for ARA in Bikol.