@inproceedings{eo-etal-2024-detecting,
    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.",
}

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.
Eo, Sugyeong Lim, Jungwoo Park, Chanjun Jung, DaHyun Koo, Seonmin Moon, Hyeonseok Seo, Jaehyung Lim, Heuiseok
Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation