Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction

Christopher Bryant, Mariano Felice, Ted Briscoe


Abstract
Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as “Good” or “Acceptable” in at least 95% of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.
Anthology ID:
P17-1074
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
793–805
Language:
URL:
https://aclanthology.org/P17-1074
DOI:
10.18653/v1/P17-1074
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P17-1074.pdf
Video:
 https://vimeo.com/234958258