@inproceedings{tang-etal-2024-mope,
    title = "{M}o{PE}: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking",
    author = "Tang, Tianwen  and
      Zhu, Tong  and
      Liu, Haodong  and
      Bai, Yin  and
      Cheng, Jia  and
      Chen, Wenliang",
    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.1012",
    pages = "11582--11592",
    abstract = "Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain transferring and partial prediction problems. To address these challenges, we propose Mixture of Prefix Experts (MoPE) to establish connections between similar slots in different domains, which strengthens the model transfer performance in unseen domains. Empirical results demonstrate that MoPE-DST achieves the joint goal accuracy of 57.13{\%} on MultiWOZ2.1 and 55.4.",
}

Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain transferring and partial prediction problems. To address these challenges, we propose Mixture of Prefix Experts (MoPE) to establish connections between similar slots in different domains, which strengthens the model transfer performance in unseen domains. Empirical results demonstrate that MoPE-DST achieves the joint goal accuracy of 57.13{\%} on MultiWOZ2.1 and 55.4.
Tang, Tianwen Zhu, Tong Liu, Haodong Bai, Yin Cheng, Jia Chen, Wenliang
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking