@inproceedings{kim-etal-2024-korean,
    title = "{K}orean Disaster Safety Information Sign Language Translation Benchmark Dataset",
    author = "Kim, Wooyoung  and
      Kim, TaeYong  and
      Kim, Byeongjin  and
      Lee, Myeong Jin MJ  and
      Lee, Gitaek  and
      Kim, Kirok  and
      Cha, Jisoo  and
      Kim, Wooju",
    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.869",
    pages = "9948--9953",
    abstract = "Sign language is a crucial means of communication for deaf communities. However, those outside deaf communities often lack understanding of sign language, leading to inadequate communication accessibility for the deaf. Therefore, sign language translation is a significantly important research area. In this context, we present a new benchmark dataset for Korean sign language translation named SSL:korean disaster Safety information Sign Language translation benchmark dataset. Korean sign language translation datasets provided by the National Information Society Agency in South Korea have faced challenges related to computational resources, heterogeneity between train and test sets, and unrefined data. To alleviate the aforementioned issue, we refine the origin data and release them. Additionally, we report experimental results of baseline using a transformer architecture. We empirically demonstrate that the baseline performance varies depending on the tokenization method applied to gloss sequences. In particular, tokenization based on characteristics of sign language outperforms tokenization considering characteristics of spoken language and tokenization utilizing statistical techniques. We release materials at our https://github.com/SSL-Sign-Language/Korean-Disaster-Safety-Information-Sign-Language-Translation-Benchmark-Dataset",
}

Sign language is a crucial means of communication for deaf communities. However, those outside deaf communities often lack understanding of sign language, leading to inadequate communication accessibility for the deaf. Therefore, sign language translation is a significantly important research area. In this context, we present a new benchmark dataset for Korean sign language translation named SSL:korean disaster Safety information Sign Language translation benchmark dataset. Korean sign language translation datasets provided by the National Information Society Agency in South Korea have faced challenges related to computational resources, heterogeneity between train and test sets, and unrefined data. To alleviate the aforementioned issue, we refine the origin data and release them. Additionally, we report experimental results of baseline using a transformer architecture. We empirically demonstrate that the baseline performance varies depending on the tokenization method applied to gloss sequences. In particular, tokenization based on characteristics of sign language outperforms tokenization considering characteristics of spoken language and tokenization utilizing statistical techniques. We release materials at our https://github.com/SSL-Sign-Language/Korean-Disaster-Safety-Information-Sign-Language-Translation-Benchmark-Dataset
Kim, Wooyoung Kim, TaeYong Kim, Byeongjin Lee, Myeong Jin MJ Lee, Gitaek Kim, Kirok Cha, Jisoo Kim, Wooju
Korean Disaster Safety Information Sign Language Translation Benchmark Dataset