论文标题
多域对话系统的域感知对话状态跟踪器
Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems
论文作者
论文摘要
在面向任务的对话系统中，对话状态跟踪器（DST）组件负责根据对话历史来预测对话的状态。当前的DST方法依赖于预定义的领域本体论，这一事实限制了它们在大规模对话剂中的有效用法，在这种情况下，DST不断需要与不断增长的服务和API进行连接。我们专注于克服这一缺点，我们提出了一个域吸引的对话状态跟踪器，该跟踪器完全由数据驱动，并且是为了预测动态服务模式的建模。提出的模型利用域和插槽信息来提取给定对话的域和插槽特定表示，然后使用此类表示来预测相应插槽的值。将这种机制与验证的语言模型（即BERT）相结合，我们的方法可以有效地学习语义关系。
In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas. The proposed model utilizes domain and slot information to extract both domain and slot specific representations for a given dialogue, and then uses such representations to predict the values of the corresponding slot. Integrating this mechanism with a pretrained language model (i.e. BERT), our approach can effectively learn semantic relations.
