@article{doi:10.1162/tacl_a_00282,
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 = {https://doi.org/10.1162/tacl_a_00282},
eprint = {https://doi.org/10.1162/tacl_a_00282},
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.}
}
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 courtney.napoles@grammarly.com for updates.