@inproceedings{forti-etal-2020-malt,
title = "{MALT}-{IT}2: A New Resource to Measure Text Difficulty in Light of {CEFR} Levels for {I}talian {L}2 Learning",
author = "Forti, Luciana and
Grego Bolli, Giuliana and
Santarelli, Filippo and
Santucci, Valentino and
Spina, Stefania",
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-1.890",
pages = "7204--7211",
abstract = "This paper presents a new resource for automatically assessing text difficulty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classifies inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the field of automatic text difficulty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly influenced the predictions.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>This paper presents a new resource for automatically assessing text difficulty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classifies inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the field of automatic text difficulty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly influenced the predictions.</abstract>
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%0 Conference Proceedings
%T MALT-IT2: A New Resource to Measure Text Difficulty in Light of CEFR Levels for Italian L2 Learning
%A Forti, Luciana
%A Grego Bolli, Giuliana
%A Santarelli, Filippo
%A Santucci, Valentino
%A Spina, Stefania
%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 English
%F forti-etal-2020-malt
%X This paper presents a new resource for automatically assessing text difficulty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classifies inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the field of automatic text difficulty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly influenced the predictions.
%U https://aclanthology.org/2020.lrec-1.890
%P 7204-7211
Markdown (Informal)
[MALT-IT2: A New Resource to Measure Text Difficulty in Light of CEFR Levels for Italian L2 Learning](https://aclanthology.org/2020.lrec-1.890) (Forti et al., LREC 2020)
ACL