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        <title>Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric</title>
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    <abstract>It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts—in what is known as the gold standard for reference-based metrics—or a separate annotated dataset to fine-tune the reference-less metric. Reference-based systems have a low correlation with human judgement, cannot capture all the ways in which a sentence can be corrected, and require substantial work to develop a test dataset. We propose a reference-less GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. The proposed system relies solely on commonly available tools. Additionally, currently available reference-less metrics do not work properly when part of a sentence is repeated as opposed to reference-based metrics. In our proposed system, we look to address issues inherent in reference-less metrics and reference-based metrics.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.239</identifier>
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%0 Conference Proceedings
%T Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric
%A Islam, Md Asadul
%A Magnani, Enrico
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F islam-magnani-2021-end
%X It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts—in what is known as the gold standard for reference-based metrics—or a separate annotated dataset to fine-tune the reference-less metric. Reference-based systems have a low correlation with human judgement, cannot capture all the ways in which a sentence can be corrected, and require substantial work to develop a test dataset. We propose a reference-less GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. The proposed system relies solely on commonly available tools. Additionally, currently available reference-less metrics do not work properly when part of a sentence is repeated as opposed to reference-based metrics. In our proposed system, we look to address issues inherent in reference-less metrics and reference-based metrics.
%R 10.18653/v1/2021.emnlp-main.239
%U https://aclanthology.org/2021.emnlp-main.239
%U https://doi.org/10.18653/v1/2021.emnlp-main.239
%P 3009-3015

Markdown (Informal)

[Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric](https://aclanthology.org/2021.emnlp-main.239) (Islam & Magnani, EMNLP 2021)

• Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric (Islam & Magnani, EMNLP 2021)

ACL

• Md Asadul Islam and Enrico Magnani. 2021. Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3009–3015, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.