A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners
MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
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In this paper, we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable ILR proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words for Arabic, Dari, English, and Pashto. We compare Support Vector Machines and the Margin-Infused Relaxed Algorithm measured by mean squared error. We provide an analysis of which features are most predictive of a given level.