User Modeling in Language Learning with Macaronic Texts

Abstract

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.