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grammarly · Oct 24, 2019 · b3b0241 · b3b0241
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+# Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
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+@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.}
+}
+```