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
this https URL

提出了一种新颖的基于预训练语言模型的任务驱动对话状态追踪方法，利用基于模式的提示和模式描述提高了性能，适用于多个基准测试（MultiWOZ 2.2、MultiWOZ 2.1 和 M2M），实现了最先进的性能。