@inproceedings{ji-etal-2024-robust,
    title = "Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation",
    author = "Ji, Seunghyun  and
      Sinulingga, Hagai Raja  and
      Kwon, Darongsae",
    editor = "Melero, Maite  and
      Sakti, Sakriani  and
      Soria, Claudia",
    booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.sigul-1.37",
    pages = "307--317",
    abstract = "Low-resourced data presents a significant challenge for neural machine translation. In most cases, the low-resourced environment is caused by high costs due to the need for domain experts or the lack of language experts. Therefore, identifying the most training-efficient data within an unsupervised setting emerges as a practical strategy. Recent research suggests that such effective data can be identified by selecting {`}appropriately complex data{'} based on its volume, providing strong intuition for unsupervised data selection. However, we have discovered that establishing criteria for unsupervised data selection remains a challenge, as the {`}appropriate level of difficulty{'} may vary depending on the data domain. We introduce a novel unsupervised data selection method named {`}Capturing Perplexing Named Entities,{'} which leverages the maximum inference entropy in translated named entities as a metric for selection. When tested with the {`}Korean-English Parallel Corpus of Specialized Domains,{'} our method served as robust guidance for identifying training-efficient data across different domains, in contrast to existing methods.",
}

Low-resourced data presents a significant challenge for neural machine translation. In most cases, the low-resourced environment is caused by high costs due to the need for domain experts or the lack of language experts. Therefore, identifying the most training-efficient data within an unsupervised setting emerges as a practical strategy. Recent research suggests that such effective data can be identified by selecting {`}appropriately complex data{'} based on its volume, providing strong intuition for unsupervised data selection. However, we have discovered that establishing criteria for unsupervised data selection remains a challenge, as the {`}appropriate level of difficulty{'} may vary depending on the data domain. We introduce a novel unsupervised data selection method named {`}Capturing Perplexing Named Entities,{'} which leverages the maximum inference entropy in translated named entities as a metric for selection. When tested with the {`}Korean-English Parallel Corpus of Specialized Domains,{'} our method served as robust guidance for identifying training-efficient data across different domains, in contrast to existing methods.
Ji, Seunghyun Sinulingga, Hagai Raja Kwon, Darongsae
Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation