User Modeling in Language Learning with Macaronic Texts
Adithya Renduchintala, Rebecca E. Knowles, Philipp Koehn, Jason M. Eisner · 2016
Foreign language learners can acquire new vocabulary by using cognate and context clues when reading.To measure such incidental comprehension, we devise an experimental framework that involves reading mixed-language "macaronic" sentences.Using data collected via Amazon Mechanical Turk, we train a graphical model to simulate a human subject's comprehension of foreign words, based on cognate clues (edit distance to an English word), context clues (pointwise mutual information), and prior exposure.Our model does a reasonable job at predicting which words a user will be able to understand, which should facilitate the automatic construction of comprehensible text for personalized foreign language education.