@inproceedings{tang-etal-2024-mope-mixture,
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.",
}
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%0 Conference Proceedings
%T MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
%A Tang, Tianwen
%A Zhu, Tong
%A Liu, Haodong
%A Bai, Yin
%A Cheng, Jia
%A Chen, Wenliang
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tang-etal-2024-mope-mixture
%X 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.
%U https://aclanthology.org/2024.lrec-main.1012
%P 11582-11592
Markdown (Informal)
[MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking](https://aclanthology.org/2024.lrec-main.1012) (Tang et al., LREC-COLING 2024)
ACL