SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction

Ryoma Yoshimura, Masahiro Kaneko, Tomoyuki Kajiwara, Mamoru Komachi


Abstract
We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC). Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluations of the system outputs because no dataset of the system output exists with manual evaluation. This study manually evaluates outputs of GEC systems to optimize the metrics. Experimental results show that the proposed metric improves correlation with the manual evaluation in both system- and sentence-level meta-evaluation. Our dataset and metric will be made publicly available.
Anthology ID:
2020.coling-main.573
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6516–6522
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.573
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.573.pdf