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        <title>How Good (really) are Grammatical Error Correction Systems?</title>
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            <namePart type="given">Paola</namePart>
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    <abstract>Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go. This analysis paper studies the performance of GEC systems relative to closest-gold – a gold reference text created relative to the output of a system. Surprisingly, we show that the real performance is 20-40 points better than standard evaluations show. Moreover, the performance remains high even when considering any of the top-10 hypotheses produced by a system. Importantly, the type of mistakes corrected by lower-ranked hypotheses differs in interesting ways from the top one, providing an opportunity to focus on a range of errors – local spelling and grammar edits vs. more complex lexical improvements. Our study shows these results in English and Russian, and thus provides a preliminary proposal for a more realistic evaluation of GEC systems.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.231</identifier>
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%0 Conference Proceedings
%T How Good (really) are Grammatical Error Correction Systems?
%A Rozovskaya, Alla
%A Roth, Dan
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F rozovskaya-roth-2021-good
%X Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go. This analysis paper studies the performance of GEC systems relative to closest-gold – a gold reference text created relative to the output of a system. Surprisingly, we show that the real performance is 20-40 points better than standard evaluations show. Moreover, the performance remains high even when considering any of the top-10 hypotheses produced by a system. Importantly, the type of mistakes corrected by lower-ranked hypotheses differs in interesting ways from the top one, providing an opportunity to focus on a range of errors – local spelling and grammar edits vs. more complex lexical improvements. Our study shows these results in English and Russian, and thus provides a preliminary proposal for a more realistic evaluation of GEC systems.
%R 10.18653/v1/2021.eacl-main.231
%U https://aclanthology.org/2021.eacl-main.231
%U https://doi.org/10.18653/v1/2021.eacl-main.231
%P 2686-2698

Markdown (Informal)

[How Good (really) are Grammatical Error Correction Systems?](https://aclanthology.org/2021.eacl-main.231) (Rozovskaya & Roth, EACL 2021)

• How Good (really) are Grammatical Error Correction Systems? (Rozovskaya & Roth, EACL 2021)

ACL

• Alla Rozovskaya and Dan Roth. 2021. How Good (really) are Grammatical Error Correction Systems?. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2686–2698, Online. Association for Computational Linguistics.