@inproceedings{horbach-etal-2020-linguistic,
title = "Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions",
author = "Horbach, Andrea and
Aldabe, Itziar and
Bexte, Marie and
Lopez de Lacalle, Oier and
Maritxalar, Montse",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.217/",
pages = "1753--1762",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions. This paper overcomes such weaknesses by a new evaluation scheme where questions from the questionnaire are structured in a hierarchical way to avoid confronting human annotators with evaluation measures that do not make sense for a certain question. We show through an annotation study that our scheme can be applied, but that expert annotators with some level of expertise are needed. We also created and evaluated two new evaluation data sets from the biology domain for Basque and German, composed of questions written by people with an educational background, which will be publicly released. Results show that manually generated questions are in general both of higher linguistic as well as pedagogic quality and that among the human generated questions, teacher-generated ones tend to be most useful."
}
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<abstract>Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions. This paper overcomes such weaknesses by a new evaluation scheme where questions from the questionnaire are structured in a hierarchical way to avoid confronting human annotators with evaluation measures that do not make sense for a certain question. We show through an annotation study that our scheme can be applied, but that expert annotators with some level of expertise are needed. We also created and evaluated two new evaluation data sets from the biology domain for Basque and German, composed of questions written by people with an educational background, which will be publicly released. Results show that manually generated questions are in general both of higher linguistic as well as pedagogic quality and that among the human generated questions, teacher-generated ones tend to be most useful.</abstract>
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%0 Conference Proceedings
%T Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions
%A Horbach, Andrea
%A Aldabe, Itziar
%A Bexte, Marie
%A Lopez de Lacalle, Oier
%A Maritxalar, Montse
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%F horbach-etal-2020-linguistic
%X Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions. This paper overcomes such weaknesses by a new evaluation scheme where questions from the questionnaire are structured in a hierarchical way to avoid confronting human annotators with evaluation measures that do not make sense for a certain question. We show through an annotation study that our scheme can be applied, but that expert annotators with some level of expertise are needed. We also created and evaluated two new evaluation data sets from the biology domain for Basque and German, composed of questions written by people with an educational background, which will be publicly released. Results show that manually generated questions are in general both of higher linguistic as well as pedagogic quality and that among the human generated questions, teacher-generated ones tend to be most useful.
%U https://aclanthology.org/2020.lrec-1.217/
%P 1753-1762
Markdown (Informal)
[Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions](https://aclanthology.org/2020.lrec-1.217/) (Horbach et al., LREC 2020)
ACL