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        <title>Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses</title>
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    <name type="personal">
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    <abstract>Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.680</identifier>
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%0 Conference Proceedings
%T Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
%A Flachs, Simon
%A Lacroix, Ophélie
%A Yannakoudakis, Helen
%A Rei, Marek
%A Søgaard, Anders
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F flachs-etal-2020-grammatical
%X Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.
%R 10.18653/v1/2020.emnlp-main.680
%U https://aclanthology.org/2020.emnlp-main.680
%U https://doi.org/10.18653/v1/2020.emnlp-main.680
%P 8467-8478

Markdown (Informal)

[Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses](https://aclanthology.org/2020.emnlp-main.680) (Flachs et al., EMNLP 2020)

• Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (Flachs et al., EMNLP 2020)

ACL

• Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, and Anders Søgaard. 2020. Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8467–8478, Online. Association for Computational Linguistics.