@inproceedings{qixiang-etal-2022-exploiting,
    title = "Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking",
    author = "Qixiang, Gao  and
      Dong, Guanting  and
      Mou, Yutao  and
      Wang, Liwen  and
      Zeng, Chen  and
      Guo, Daichi  and
      Sun, Mingyang  and
      Xu, Weiran",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.157",
    pages = "2460--2465",
    abstract = "Collecting dialogue data with domain-slot-value labels for dialogue state tracking (DST) could be a costly process. In this paper, we propose a novel framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST. Specifically, we design an extraction module to extract domain-slot related verbs and nouns in the dialogue. Then, we integrates them into the description, which aims to prompt the model to identify the slot information. Furthermore, we introduce a random sampling strategy to improve the domain generalization ability of the model. We utilize a pre-trained model to encode contexts and description and generates answers with an auto-regressive manner. Experimental results show that our approaches substantially outperform the existing few-shot DST methods on MultiWOZ and gain strong improvements on the slot accuracy comparing to existing slot description methods.",
}