双教师和双提示池为少数镜头对话状态跟踪
IEEE transactions on neural networks and learning systems
|February 18, 2026
概括
双教师和双提示池模型 (DDP-DST) 通过使用双教师进行伪标签和动态提示池来提高更好的适应性来增强少数镜头对话状态跟踪. 这种方法可以提高面向任务的对话系统的性能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 对话状态跟踪 (DST) 对于面向任务的对话 (ToD) 系统至关重要.
- 现有的少数拍摄的DST模型在语义信息利用和适应性方面扎.
- 挑战包括利用相关信息并适应各种对话场景.
研究的目的:
- 建议双教师和双提示池模型用于少数射击的DST (DDP-DST).
- 增强语义和语法信息处理,以改进伪标签生成.
- 在有限的数据场景中改进状态值生成和整体DST准确性.
主要方法:
- 构建双教师模型,从互补的角度生成伪标签.
- 采用自我培训来完善国家价值的产生.
- 使用双提示符微调策略与动态提示符池用于自适应式提示符生成.
- 纳入重建错误 (RE) 来提高模型的准确性.
主要成果:
- 在MultiWOZ 2.1数据集上,DDP-DST表现出卓越的性能.
- 与基线模型 (SM2-3b,DS2,SVAG) 相比,联合目标准确度 (JGA) 的平均改善率为4.3%,2.4%和2.0%.
- 在少数镜头设置中表现优于较大的模型,尽管参数少于10亿.
结论:
- DDP-DST有效地解决了现有的少数射击DST模型的局限性.
- 拟议的双教师和双提示方法显著提高了DST的准确性和适应性.
- 在短暂的场景中实现竞争性和通常优异的性能,提供高效的解决方案.
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