@inproceedings{yamashita-etal-2020-cross,
title = "Cross-lingual Transfer Learning for Grammatical Error Correction",
author = "Yamashita, Ikumi and
Katsumata, Satoru and
Kaneko, Masahiro and
Imankulova, Aizhan and
Komachi, Mamoru",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.415",
doi = "10.18653/v1/2020.coling-main.415",
pages = "4704--4715",
abstract = "In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident. Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.",
}
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<abstract>In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident. Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.</abstract>
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%0 Conference Proceedings
%T Cross-lingual Transfer Learning for Grammatical Error Correction
%A Yamashita, Ikumi
%A Katsumata, Satoru
%A Kaneko, Masahiro
%A Imankulova, Aizhan
%A Komachi, Mamoru
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F yamashita-etal-2020-cross
%X In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident. Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.
%R 10.18653/v1/2020.coling-main.415
%U https://aclanthology.org/2020.coling-main.415
%U https://doi.org/10.18653/v1/2020.coling-main.415
%P 4704-4715
Markdown (Informal)
[Cross-lingual Transfer Learning for Grammatical Error Correction](https://aclanthology.org/2020.coling-main.415) (Yamashita et al., COLING 2020)
ACL
- Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, and Mamoru Komachi. 2020. Cross-lingual Transfer Learning for Grammatical Error Correction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4704–4715, Barcelona, Spain (Online). International Committee on Computational Linguistics.