@inproceedings{purwarianti-etal-2025-nusadialogue,
    title = "{N}usa{D}ialogue: Dialogue Summarization and Generation for Underrepresented and Extremely Low-Resource Languages",
    author = "Purwarianti, Ayu  and
      Adhista, Dea  and
      Baptiso, Agung  and
      Mahfuzh, Miftahul  and
      Sabila, Yusrina  and
      Adila, Aulia  and
      Cahyawijaya, Samuel  and
      Aji, Alham Fikri",
    editor = "Wijaya, Derry  and
      Aji, Alham Fikri  and
      Vania, Clara  and
      Winata, Genta Indra  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of the Second Workshop in South East Asian Language Processing",
    month = jan,
    year = "2025",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.sealp-1.8/",
    pages = "82--100",
    abstract = "Developing dialogue summarization for extremely low-resource languages is a challenging task. We introduce NusaDialogue, a dialogue summarization dataset for three underrepresented languages in the Malayo-Polynesian language family: Minangkabau, Balinese, and Buginese. NusaDialogue covers 17 topics and 185 subtopics, with annotations provided by 73 native speakers. Additionally, we conducted experiments using fine-tuning on a specifically designed medium-sized language model for Indonesian, as well as zero- and few-shot learning on various multilingual large language models (LLMs). The results indicate that, for extremely low-resource languages such as Minangkabau, Balinese, and Buginese, the fine-tuning approach yields significantly higher performance compared to zero- and few-shot prompting, even when applied to LLMs with considerably larger parameter sizes."
}