@inproceedings{eo-etal-2024-detecting-critical,
title = "Detecting Critical Errors Considering Cross-Cultural Factors in {E}nglish-{K}orean Translation",
author = "Eo, Sugyeong and
Lim, Jungwoo and
Park, Chanjun and
Jung, DaHyun and
Koo, Seonmin and
Moon, Hyeonseok and
Seo, Jaehyung and
Lim, Heuiseok",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.421",
pages = "4705--4716",
abstract = "Recent machine translation (MT) systems have overcome language barriers for a wide range of users, yet they still carry the risk of critical meaning deviation. Critical error detection (CED) is a task that identifies an inherent risk of catastrophic meaning distortions in the machine translation output. With the importance of reflecting cultural elements in detecting critical errors, we introduce the culture-aware {``}Politeness{''} type in detecting English-Korean critical translation errors. Besides, we facilitate two tasks by providing multiclass labels: critical error detection and critical error type classification (CETC). Empirical evaluations reveal that our introduced data augmentation approach using a newly presented perturber significantly outperforms existing baselines in both tasks. Further analysis highlights the significance of multiclass labeling by demonstrating its superior effectiveness compared to binary labels.",
}
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<abstract>Recent machine translation (MT) systems have overcome language barriers for a wide range of users, yet they still carry the risk of critical meaning deviation. Critical error detection (CED) is a task that identifies an inherent risk of catastrophic meaning distortions in the machine translation output. With the importance of reflecting cultural elements in detecting critical errors, we introduce the culture-aware “Politeness” type in detecting English-Korean critical translation errors. Besides, we facilitate two tasks by providing multiclass labels: critical error detection and critical error type classification (CETC). Empirical evaluations reveal that our introduced data augmentation approach using a newly presented perturber significantly outperforms existing baselines in both tasks. Further analysis highlights the significance of multiclass labeling by demonstrating its superior effectiveness compared to binary labels.</abstract>
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%0 Conference Proceedings
%T Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation
%A Eo, Sugyeong
%A Lim, Jungwoo
%A Park, Chanjun
%A Jung, DaHyun
%A Koo, Seonmin
%A Moon, Hyeonseok
%A Seo, Jaehyung
%A Lim, Heuiseok
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F eo-etal-2024-detecting-critical
%X Recent machine translation (MT) systems have overcome language barriers for a wide range of users, yet they still carry the risk of critical meaning deviation. Critical error detection (CED) is a task that identifies an inherent risk of catastrophic meaning distortions in the machine translation output. With the importance of reflecting cultural elements in detecting critical errors, we introduce the culture-aware “Politeness” type in detecting English-Korean critical translation errors. Besides, we facilitate two tasks by providing multiclass labels: critical error detection and critical error type classification (CETC). Empirical evaluations reveal that our introduced data augmentation approach using a newly presented perturber significantly outperforms existing baselines in both tasks. Further analysis highlights the significance of multiclass labeling by demonstrating its superior effectiveness compared to binary labels.
%U https://aclanthology.org/2024.lrec-main.421
%P 4705-4716
Markdown (Informal)
[Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation](https://aclanthology.org/2024.lrec-main.421) (Eo et al., LREC-COLING 2024)
ACL
- Sugyeong Eo, Jungwoo Lim, Chanjun Park, DaHyun Jung, Seonmin Koo, Hyeonseok Moon, Jaehyung Seo, and Heuiseok Lim. 2024. Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4705–4716, Torino, Italia. ELRA and ICCL.