Multi-lingual Text Leveling

Salim Roukos, Jerome Quin, Todd Ward

Abstract: Determining the language proficiency level required to understand a given text is a key requirement in vetting documents for use in second language learning. In this work, we describe our approach for developing an automatic text analytic to estimate the text difficulty level using the Interagency Language Roundtable (ILR) proficiency scale. The approach we take is to use machine translation to translate a non-English document into English and then use an English language trained ILR level detector. We achieve good results in predicting ILR levels with both human and machine translation of Farsi documents. We also report results on text leveling prediction on human translations into English of documents from 54 languages.