@inproceedings{jung-etal-2023-bring,
title = "Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations",
author = "Jung, Baikjin and
Lee, Myungji and
Lee, Jong-Hyeok and
Kim, Yunsu",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.122",
doi = "10.18653/v1/2023.acl-short.122",
pages = "1433--1441",
abstract = "Automatic postediting (APE) is an automated process to refine a given machine translation (MT). Recent findings present that existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English{--}German: the better the given MT is, the harder it is to decide what parts to edit and how to fix these errors. One possible solution to this problem is to instill deeper knowledge about the target language into the model. Thus, we propose a linguistically motivated method of regularization that is expected to enhance APE models{'} understanding of the target language: a loss function that encourages symmetric self-attention on the given MT. Our analysis of experimental results demonstrates that the proposed method helps improving the state-of-the-art architecture{'}s APE quality for high-quality MTs.",
}
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<abstract>Automatic postediting (APE) is an automated process to refine a given machine translation (MT). Recent findings present that existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English–German: the better the given MT is, the harder it is to decide what parts to edit and how to fix these errors. One possible solution to this problem is to instill deeper knowledge about the target language into the model. Thus, we propose a linguistically motivated method of regularization that is expected to enhance APE models’ understanding of the target language: a loss function that encourages symmetric self-attention on the given MT. Our analysis of experimental results demonstrates that the proposed method helps improving the state-of-the-art architecture’s APE quality for high-quality MTs.</abstract>
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%0 Conference Proceedings
%T Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations
%A Jung, Baikjin
%A Lee, Myungji
%A Lee, Jong-Hyeok
%A Kim, Yunsu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jung-etal-2023-bring
%X Automatic postediting (APE) is an automated process to refine a given machine translation (MT). Recent findings present that existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English–German: the better the given MT is, the harder it is to decide what parts to edit and how to fix these errors. One possible solution to this problem is to instill deeper knowledge about the target language into the model. Thus, we propose a linguistically motivated method of regularization that is expected to enhance APE models’ understanding of the target language: a loss function that encourages symmetric self-attention on the given MT. Our analysis of experimental results demonstrates that the proposed method helps improving the state-of-the-art architecture’s APE quality for high-quality MTs.
%R 10.18653/v1/2023.acl-short.122
%U https://aclanthology.org/2023.acl-short.122
%U https://doi.org/10.18653/v1/2023.acl-short.122
%P 1433-1441
Markdown (Informal)
[Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations](https://aclanthology.org/2023.acl-short.122) (Jung et al., ACL 2023)
ACL