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.

中文翻译：

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多域对话系统的域感知对话状态跟踪器

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预测对话状态。当前的 DST 方法依赖于预定义的域本体，这一事实限制了它们在大规模会话代理中的有效使用，其中 DST 不断需要与不断增加的服务和 API 接口。为了克服这个缺点，我们提出了一个域感知对话状态跟踪器，它是完全数据驱动的，并且被建模以预测动态服务模式。所提出的模型利用域和槽信息来提取给定对话的域和槽特定表示，然后使用这些表示来预测相应槽的值。将此机制与预训练的语言模型（即