Annotation and Classification of Sentence-level Revision Improvement

Tazin Afrin, Diane Litman


Abstract
Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.
Anthology ID:
W18-0528
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
240–246
Language:
URL:
https://www.aclweb.org/anthology/W18-0528
DOI:
10.18653/v1/W18-0528
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0528.pdf