论文标题
以目标为导向的多任务对话状态跟踪器
Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker
论文作者
论文摘要
对话状态跟踪（DST）是Alexa或Siri等虚拟助手的核心组成部分。为了完成各种任务，这些助手需要支持越来越多的服务和API。第8对话系统技术挑战的模式指导的状态跟踪轨道突出了看不见的服务的DST问题。组织者介绍了具有多域对话的模式引导的对话（SGD）数据集，并发布了零摄像的对话状态跟踪模型。在这项工作中，我们提出了一个以目标为导向的多任务对话状态跟踪器（GOLOMB），该对话是受架构启发的用于阅读理解问答系统的架构。模型“查询”对话历史记录具有插槽和服务的描述以及插槽的可能值。这允许在多域对话中传输插槽值，并具有缩放插槽类型的功能。我们的模型在SGD数据集上实现了53.97％的联合目标准确性，表现优于基线模型。
Dialogue State Tracking (DST) is a core component of virtual assistants such as Alexa or Siri. To accomplish various tasks, these assistants need to support an increasing number of services and APIs. The Schema-Guided State Tracking track of the 8th Dialogue System Technology Challenge highlighted the DST problem for unseen services. The organizers introduced the Schema-Guided Dialogue (SGD) dataset with multi-domain conversations and released a zero-shot dialogue state tracking model. In this work, we propose a GOaL-Oriented Multi-task BERT-based dialogue state tracker (GOLOMB) inspired by architectures for reading comprehension question answering systems. The model "queries" dialogue history with descriptions of slots and services as well as possible values of slots. This allows to transfer slot values in multi-domain dialogues and have a capability to scale to unseen slot types. Our model achieves a joint goal accuracy of 53.97% on the SGD dataset, outperforming the baseline model.
