@inproceedings{morgado-da-costa-etal-2020-automated,
title = "Automated Writing Support Using Deep Linguistic Parsers",
author = "Morgado da Costa, Lu{\'i}s and
V P Winder, Roger and
Li, Shu Yun and
Lin Tzer Liang, Benedict Christopher and
Mackinnon, Joseph and
Bond, Francis",
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-1.46/",
pages = "369--377",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "This paper introduces a new web system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. The system is being developed as a pedagogical tool for English Scientific Writing. It uses both general NLP methods and high precision parsers to check student assignments before they are submitted for grading. Instead of generalized error detection, our system aims to identify, with high precision, specific classes of problems that are known to be common among engineering students. Rather than correct the errors, our system generates constructive feedback to help students identify and correct them on their own. A preliminary evaluation of the system`s in-class performance has shown measurable improvements in the quality of student assignments."
}
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%0 Conference Proceedings
%T Automated Writing Support Using Deep Linguistic Parsers
%A Morgado da Costa, Luís
%A V P Winder, Roger
%A Li, Shu Yun
%A Lin Tzer Liang, Benedict Christopher
%A Mackinnon, Joseph
%A Bond, Francis
%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 morgado-da-costa-etal-2020-automated
%X This paper introduces a new web system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. The system is being developed as a pedagogical tool for English Scientific Writing. It uses both general NLP methods and high precision parsers to check student assignments before they are submitted for grading. Instead of generalized error detection, our system aims to identify, with high precision, specific classes of problems that are known to be common among engineering students. Rather than correct the errors, our system generates constructive feedback to help students identify and correct them on their own. A preliminary evaluation of the system‘s in-class performance has shown measurable improvements in the quality of student assignments.
%U https://aclanthology.org/2020.lrec-1.46/
%P 369-377
Markdown (Informal)
[Automated Writing Support Using Deep Linguistic Parsers](https://aclanthology.org/2020.lrec-1.46/) (Morgado da Costa et al., LREC 2020)
ACL
- Luís Morgado da Costa, Roger V P Winder, Shu Yun Li, Benedict Christopher Lin Tzer Liang, Joseph Mackinnon, and Francis Bond. 2020. Automated Writing Support Using Deep Linguistic Parsers. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 369–377, Marseille, France. European Language Resources Association.