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

author = {Napoles, Courtney and Nădejde, Maria and Tetreault, Joel},
title = {Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {551-566},
year = {2019},
doi = {10.1162/tacl_a_00282},
URL = {},
eprint = {},
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.}

Repository Contents


data/ contains the dev and test splits, with a subdirectory for each domain containing

  • the original sentences (source)
  • system outputs (amu, lstm, lstm-r, marian, nus, transformer)
  • human corrections (ref[0-3])
  • negative control used for collecting human ratings (source+error)

Domains are fce, wiki, and yahoo.

DOMAIN-corpus-scores.csv has the mean human rating for each system for that domain. DOMAIN-segment-scores.csv has the mean human rating by sentence for each system.


Coming soon. Please watch this repository or email for updates.

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