# Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners

arXiv: https://arxiv.org/abs/1609.05180 · HTML: https://mlsanity.com/paper/1609.05180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2016.

## Built on by (most cited first, 3 of 3)

- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)
- [GrammarTagger: A Multilingual, Minimally-Supervised Grammar Profiler for Language Education](https://mlsanity.com/paper/2104.03190/) (2021)
- [Semi-Supervised Joint Estimation of Word and Document Readability](https://mlsanity.com/paper/2104.13103/) (2021)

## Abstract (arXiv)

Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental classification results show that grammatical template features significantly improve text difficulty prediction accuracy over baseline readability features by 7.4%. Moreover, we build a simple and human-understandable text difficulty evaluation approach with 87.7% accuracy, using only 5 grammatical template features.