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            <title>Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>Grammatical error correction (GEC) is a challenging task for non-native second language (L2) learners and learning machines. Data-driven GEC learning requires as much human-annotated genuine training data as possible. However, it is difficult to produce larger-scale human-annotated data, and synthetically generated large-scale parallel training data is valuable for GEC systems. In this paper, we propose a method for rebuilding a corpus of synthetic parallel data using target sentences predicted by a GEC model to improve performance. Experimental results show that our proposed pre-training outperforms that on the original synthetic datasets. Moreover, it is also shown that our proposed training without human-annotated L2 learners’ corpora is as practical as conventional full pipeline training with both synthetic datasets and L2 learners’ corpora in terms of accuracy.</abstract>
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%0 Conference Proceedings
%T Training for Grammatical Error Correction Without Human-Annotated L2 Learners’ Corpora
%A Oda, Mikio
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F oda-2023-training
%X Grammatical error correction (GEC) is a challenging task for non-native second language (L2) learners and learning machines. Data-driven GEC learning requires as much human-annotated genuine training data as possible. However, it is difficult to produce larger-scale human-annotated data, and synthetically generated large-scale parallel training data is valuable for GEC systems. In this paper, we propose a method for rebuilding a corpus of synthetic parallel data using target sentences predicted by a GEC model to improve performance. Experimental results show that our proposed pre-training outperforms that on the original synthetic datasets. Moreover, it is also shown that our proposed training without human-annotated L2 learners’ corpora is as practical as conventional full pipeline training with both synthetic datasets and L2 learners’ corpora in terms of accuracy.
%R 10.18653/v1/2023.bea-1.38
%U https://aclanthology.org/2023.bea-1.38/
%U https://doi.org/10.18653/v1/2023.bea-1.38
%P 455-465

Markdown (Informal)

[Training for Grammatical Error Correction Without Human-Annotated L2 Learners’ Corpora](https://aclanthology.org/2023.bea-1.38/) (Oda, BEA 2023)

• Training for Grammatical Error Correction Without Human-Annotated L2 Learners’ Corpora (Oda, BEA 2023)

ACL

• Mikio Oda. 2023. Training for Grammatical Error Correction Without Human-Annotated L2 Learners’ Corpora. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 455–465, Toronto, Canada. Association for Computational Linguistics.