Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses

Courtney Napoles, Maria Nădejde, Joel Tetreault


Abstract
Until now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays. This paper enables GEC development on text written by native speakers by providing a new data set and metric. We present a multiple-reference test corpus for GEC that includes 4,000 sentences in two new domains (formal and informal writing by native English speakers) and 2,000 sentences from a diverse set of non-native student writing. We also collect human judgments of several GEC systems on this new test set and perform a meta-evaluation, assessing how reliable automatic metrics are across these domains. We find that commonly used GEC metrics have inconsistent performance across domains, and therefore we propose a new ensemble metric that is robust on all three domains of text.
Anthology ID:
Q19-1032
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
March
Year:
2019
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
551–566
Language:
URL:
https://www.aclweb.org/anthology/Q19-1032
DOI:
10.1162/tacl_a_00282
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PDF:
http://aclanthology.lst.uni-saarland.de/Q19-1032.pdf