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Harvard
Mitkov, R, Ha, LA, Maslak, H
, Ranasinghe, T & Sosoni, V 2023,
Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks. in V Yaneva & M von Davier (eds),
Advancing Natural Language Processing in Educational Assessment. NCME APPLICATIONS OF EDUCATIONAL MEASUREMENT AND ASSESSMENT, Routledge, London, pp. 77-89. <
https://www.routledge.com/Advancing-Natural-Language-Processing-in-Educational-Assessment/von-Davier-Yaneva/p/book/9781032244525>
APA
Mitkov, R., Ha, L. A., Maslak, H.
, Ranasinghe, T., & Sosoni, V. (2023).
Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks. In V. Yaneva, & M. von Davier (Eds.),
Advancing Natural Language Processing in Educational Assessment (pp. 77-89). (NCME APPLICATIONS OF EDUCATIONAL MEASUREMENT AND ASSESSMENT). Routledge.
https://www.routledge.com/Advancing-Natural-Language-Processing-in-Educational-Assessment/von-Davier-Yaneva/p/book/9781032244525
Vancouver
Author
Bibtex
@inbook{3aabc7e308f64828a0d4e098a163acf0,
title = "Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks",
abstract = "Deep learning (DL) has taken the research community by storm. The employment of DL techniques can be seen everywhere, DL has been pervasive as methodology, and the area of multiple-choice question generation is also joining the club of DL applications. In this chapter, we propose a DL methodology to solve a problem which to our knowledge has not been tackled before: the generation of multiple-choice questions (MCQs) which are based on the information of not one sentence only, but on a sequence of sentences.",
author = "Ruslan Mitkov and Ha, {Le An} and Halyna Maslak and Tharindu Ranasinghe and Vilelmini Sosoni",
year = "2023",
month = jun,
day = "5",
language = "English",
isbn = "9781032203904",
series = "NCME APPLICATIONS OF EDUCATIONAL MEASUREMENT AND ASSESSMENT",
publisher = "Routledge",
pages = "77--89",
editor = "Victoria Yaneva and {von Davier}, Matthias",
booktitle = "Advancing Natural Language Processing in Educational Assessment",
}
RIS
TY - CHAP
T1 - Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks
AU - Mitkov, Ruslan
AU - Ha, Le An
AU - Maslak, Halyna
AU - Ranasinghe, Tharindu
AU - Sosoni, Vilelmini
PY - 2023/6/5
Y1 - 2023/6/5
N2 - Deep learning (DL) has taken the research community by storm. The employment of DL techniques can be seen everywhere, DL has been pervasive as methodology, and the area of multiple-choice question generation is also joining the club of DL applications. In this chapter, we propose a DL methodology to solve a problem which to our knowledge has not been tackled before: the generation of multiple-choice questions (MCQs) which are based on the information of not one sentence only, but on a sequence of sentences.
AB - Deep learning (DL) has taken the research community by storm. The employment of DL techniques can be seen everywhere, DL has been pervasive as methodology, and the area of multiple-choice question generation is also joining the club of DL applications. In this chapter, we propose a DL methodology to solve a problem which to our knowledge has not been tackled before: the generation of multiple-choice questions (MCQs) which are based on the information of not one sentence only, but on a sequence of sentences.
M3 - Chapter
SN - 9781032203904
SN - 9781032244525
T3 - NCME APPLICATIONS OF EDUCATIONAL MEASUREMENT AND ASSESSMENT
SP - 77
EP - 89
BT - Advancing Natural Language Processing in Educational Assessment
A2 - Yaneva, Victoria
A2 - von Davier, Matthias
PB - Routledge
CY - London
ER -
