2016 COLING COLING 2016

Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners

Abstract

AbstractLanguage 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.

🌉 Interdisciplinary Bridge — Interdisciplinary and Natural Language Processing
📈 Trend Setter — Education
🧭 Keyword Pioneer — text difficulty
🐣 Hot Topic Early Bird — feature engineering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio