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 language | English |
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Title of host publication | Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
Place of Publication | Berlin, Germany |
Publisher | Association for Computational Linguistics |
Pages | 1859-1869 |
Number of pages | 11 |
ISBN (Print) | 978-1-945626-00-5 |
DOIs | |
Publication status | Published - 1 Aug 2016 |
Event | 54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany Duration: 7 Aug 2016 → 12 Aug 2016 https://mirror.aclweb.org/acl2016/ |
Conference
Conference | 54th Annual Meeting of the Association for Computational Linguistics |
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Abbreviated title | ACL 2016 |
Country/Territory | Germany |
City | Berlin |
Period | 7/08/16 → 12/08/16 |
Internet address |