Automated Writing Support Using Deep Linguistic Parsers

Luís Morgado da Costa, Roger V P Winder, Shu Yun Li, Benedict Christopher Lin Tzer Liang, Joseph Mackinnon, Francis Bond


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.
Anthology ID:
2020.lrec-1.46
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
369–377
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.46
DOI:
Bibkey:
Cite (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 12th Language Resources and Evaluation Conference, pages 369–377, Marseille, France. European Language Resources Association.
Cite (Informal):
Automated Writing Support Using Deep Linguistic Parsers (Morgado da Costa et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.46.pdf