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BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer
[kaggle] 3rd place solution
基于 MultiWOZ 数据集生成反事实,检测 DST 模型的鲁棒性。
DiCoS
[25]多领域对话系统的领域感知对话状态跟踪器
对话摘要作为对话状态(DS2),用于少样本对话状态跟踪的模板引导摘要
[19]使用模式驱动提示语言模型的对话状态跟踪
MetaASSIST
Slot Self-Attentive Dialogue State Tracking
使用元学习的少样本对话状态跟踪
Code base of In-Context Learning for Dialogue State tracking
Java设计模式
[27] 通过继承槽池中的槽值来跟踪对话状态
MultiWOZ Dataset
MultiWOZ 2.4
[26] 非自回归对话状态跟踪
[36]SAVN
The Schema-Guided Dialogue Dataset
[29]
[37]Task-Optimized Adapters for an End-to-End Dialogue System Paper Code
[30] TRADE
[31]跨任务迁移的零样本对话状态跟踪
[32]Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
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