User Modeling in Language Learning with Macaronic Texts

Adithya Renduchintala, Rebecca Knowles, Philipp Koehn, Jason Eisner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationBerlin, Germany
PublisherAssociation for Computational Linguistics
Pages1859-1869
Number of pages11
ISBN (Print)978-1-945626-00-5
DOIs
Publication statusPublished - 1 Aug 2016
Event54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016
https://mirror.aclweb.org/acl2016/

Conference

Conference54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016
Country/TerritoryGermany
CityBerlin
Period7/08/1612/08/16
Internet address

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