User Modeling in Language Learning with Macaronic Texts

Adithya Renduchintala; Rebecca Knowles; Philipp Koehn; Jason Eisner

doi:10.18653/v1/P16-1175

User Modeling in Language Learning with Macaronic Texts

Adithya Renduchintala, Rebecca Knowles, Philipp Koehn, Jason Eisner

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
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	https://doi.org/10.18653/v1/P16-1175
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
Abbreviated title	ACL 2016
Country/Territory	Germany
City	Berlin
Period	7/08/16 → 12/08/16
Internet address	https://mirror.aclweb.org/acl2016/

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10.18653/v1/P16-1175Licence: Creative Commons: Attribution (CC-BY)

P16-1175Final published version, 399 KBLicence: Creative Commons: Attribution (CC-BY)

http://www.aclweb.org/anthology/P16-1175Licence: Creative Commons: Attribution (CC-BY)

Cite this

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title = "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{\textquoteright}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. ",

author = "Adithya Renduchintala and Rebecca Knowles and Philipp Koehn and Jason Eisner",

year = "2016",

month = aug,

day = "1",

doi = "10.18653/v1/P16-1175",

language = "English",

isbn = "978-1-945626-00-5",

pages = "1859--1869",

booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",

publisher = "Association for Computational Linguistics",

note = "54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 ; Conference date: 07-08-2016 Through 12-08-2016",

url = "https://mirror.aclweb.org/acl2016/",

}

Renduchintala, A, Knowles, R, Koehn, P & Eisner, J 2016, User Modeling in Language Learning with Macaronic Texts. in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pp. 1859-1869, 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 7/08/16. https://doi.org/10.18653/v1/P16-1175

User Modeling in Language Learning with Macaronic Texts. / Renduchintala, Adithya; Knowles, Rebecca; Koehn, Philipp et al.

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany : Association for Computational Linguistics, 2016. p. 1859-1869.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - User Modeling in Language Learning with Macaronic Texts

AU - Renduchintala, Adithya

AU - Knowles, Rebecca

AU - Koehn, Philipp

AU - Eisner, Jason

PY - 2016/8/1

Y1 - 2016/8/1

N2 - 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.

AB - 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.

U2 - 10.18653/v1/P16-1175

DO - 10.18653/v1/P16-1175

M3 - Conference contribution

SN - 978-1-945626-00-5

SP - 1859

EP - 1869

BT - Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

PB - Association for Computational Linguistics

CY - Berlin, Germany

T2 - 54th Annual Meeting of the Association for Computational Linguistics

Y2 - 7 August 2016 through 12 August 2016

ER -

User Modeling in Language Learning with Macaronic Texts

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