@inproceedings{liu-etal-2023-enhancing-multilingual,
    title = "Enhancing Multilingual Document-Grounded Dialogue Using Cascaded Prompt-Based Post-Training Models",
    author = "Liu, Jun  and
      Cheng, Shuang  and
      Zhou, Zineng  and
      Gu, Yang  and
      Ye, Jian  and
      Luo, Haiyong",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.dialdoc-1.5",
    doi = "10.18653/v1/2023.dialdoc-1.5",
    pages = "44--51",
    abstract = "The Dialdoc23 shared task presents a Multilingual Document-Grounded Dialogue Systems (MDGDS) challenge, where system responses are generated in multiple languages using user{'}s queries, historical dialogue records and relevant passages. A major challenge for this task is the limited training data available in low-resource languages such as French and Vietnamese. In this paper, we propose Cascaded Prompt-based Post-training Models, dividing the task into three subtasks: Retrieval, Reranking and Generation. We conduct post-training on high-resource language such as English and Chinese to enhance performance of low-resource languages by using the similarities of languages. Additionally, we utilize the prompt method to activate model{'}s ability on diverse languages within the dialogue domain and explore which prompt is a good prompt. Our comprehensive experiments demonstrate the effectiveness of our proposed methods, which achieved the first place on the leaderboard with a total score of 215.40 in token-level F1, SacreBleu, and Rouge-L metrics.",
}

The Dialdoc23 shared task presents a Multilingual Document-Grounded Dialogue Systems (MDGDS) challenge, where system responses are generated in multiple languages using user{'}s queries, historical dialogue records and relevant passages. A major challenge for this task is the limited training data available in low-resource languages such as French and Vietnamese. In this paper, we propose Cascaded Prompt-based Post-training Models, dividing the task into three subtasks: Retrieval, Reranking and Generation. We conduct post-training on high-resource language such as English and Chinese to enhance performance of low-resource languages by using the similarities of languages. Additionally, we utilize the prompt method to activate model{'}s ability on diverse languages within the dialogue domain and explore which prompt is a good prompt. Our comprehensive experiments demonstrate the effectiveness of our proposed methods, which achieved the first place on the leaderboard with a total score of 215.40 in token-level F1, SacreBleu, and Rouge-L metrics.
Liu, Jun Cheng, Shuang Zhou, Zineng Gu, Yang Ye, Jian Luo, Haiyong
Enhancing Multilingual Document-Grounded Dialogue Using Cascaded Prompt-Based Post-Training Models