Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners
This work addresses the challenge of providing appropriately difficult texts for language learners, which is incremental as it enhances existing methods by incorporating grammatical features.
The paper tackled the problem of evaluating text difficulty for language learners by introducing grammatical templates as features, improving prediction accuracy by 7.4% over baseline readability features and achieving 87.7% accuracy with a simple approach using only 5 features.
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.