动态融合对话历史与领域知识，提升多轮对话状态追踪精度
Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking
- 用对比学习编码对话历史，按相关性筛选关键槽位
- 通过结构化槽位信息生成上下文提示，增强状态追踪准确性
- 在多个基准上显著提升精度与泛化能力，适合复杂对话场景
任务导向型对话模型的性能高度依赖于对话状态追踪能力，即在多轮交互中记录并更新用户信息。然而，当前多领域对话状态追踪面临两大挑战：难以有效建模对话历史，以及标注数据稀缺，二者均限制了模型表现。为此，我们提出一种适用于多领域对话状态追踪的动态知识融合框架。该模型分两阶段运行：首先，采用对比学习训练的仅编码器网络对对话历史和候选槽位进行编码，并根据相关性得分筛选出相关槽位；其次，动态知识融合利用所选槽位的结构化信息作为上下文提示，提升对话状态追踪的准确性和一致性。该设计实现了对话上下文与领域知识的更优整合。在多个多领域对话基准上的实验结果表明，该方法显著提升了追踪准确率与泛化能力，验证了其在复杂对话场景中的有效性。
原文摘要 · Abstract (English)
The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions. However, current multi-domain DST encounters two key challenges: the difficulty of effectively modeling dialogue history and the limited availability of annotated data, both of which hinder model performance. To tackle the aforementioned problems, we develop a dynamic knowledge fusion framework applicable to multi-domain DST. The model operates in two stages: first, an encoder-only network trained with contrastive learning encodes dialogue history and candidate slots, selecting relevant slots based on correlation scores; second, dynamic knowledge fusion leverages the structured information of selected slots as contextual prompts to enhance the accuracy and consistency of dialogue state tracking. This design enables more accurate integration of dialogue context and domain knowledge. Results obtained from multi-domain dialogue benchmarks indicate that our method notably improves both tracking accuracy and generalization, validating its capability in handling complex dialogue scenarios.
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