@inproceedings{Hu-etal-2023-ji
    author = {胡, 峻源  and  周, 小璐  and  谭, 龙},
    author = {Junyuan, Hu  and  Xiaolu, Zhou  and  Long, Tan},
    title = {基于缓解错误传递策略的对话状态跟踪(Dialogue State Tracking Based on Error Propagation Mitigation Strategy)},
    booktitle = {"Findings of the Proceedings of the 22nd China National Conference on Computational Linguistics"},
    month = {"August"},
    year = {"2023"},
    address = {"Harbin, China"},
    publisher = {"Chinese Information Processing Society of China"},
    url = {https://aclanthology.org/2023.findings-ccl-1.2},
    pages = {13--23},
    abstract = {"对话状态跟踪模块是任务型对话系统的核心组件。现有的一些对话状态跟踪方法基于上一轮的对话状态生成轮级状态，存在错误传递的问题，会对后续预测产生影响。因此本文提出了个基于缓解错误传递策略的对话状态跟踪模型，该模型使用对话级状态作为预测目标，在模型训练时以一定的概率随机删除前一轮次的对话状态，迫使模型在不完全可信的对话状态信息中学会纠正错误。本文在嘈杂(MultiWOZ 2.1）和洁净(MultiWOZ 2.4）数据集的实验表明，该模型相比较于基线模型有更好的错误修正能力，模型的联合准确率(MultiWOZ 2.4）达到了70.95%的良好性能表现。"},
    language = "Chinese",
}