Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.

翻译：以任务为导向的对话系统经常使用对话状态跟踪来代表用户的意图,这涉及到填补预先确定的空位的值。提出了许多办法,经常使用与特殊用途分类者具体任务有关的结构。最近,利用以预先培训的语言模式为基础的更一般性结构取得了良好的成果。在这里,我们采用了一种新的语言模式模式模式模式,采用由计划驱动的、迅速提供任务认知历史编码的方法,用于绝对空位和非空位。我们进一步改进了业绩,通过以计划描述来加快速度,这是自然产生的一个日常知识来源。我们的纯基因化系统在多WOZ2.2上取得了最先进的业绩,并在另外两个基准(多WOZ2.1和M2M)上取得了竞争性业绩。数据和代码将在https://github.com/chiahsuan156/DST-as-Promptinging上提供。