MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
Tianwen Tang, Tong Zhu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen
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.- Anthology ID:
- 2024.lrec-main.1012
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 11582–11592
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1012
- DOI:
- Cite (ACL):
- Tianwen Tang, Tong Zhu, Haodong Liu, Yin Bai, Jia Cheng, and Wenliang Chen. 2024. MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11582–11592, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (Tang et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1012.pdf