We describe an approach to task-oriented dialogue in which dialogue state is
represented as a dataflow graph. A dialogue agent maps each user utterance to a
program that extends this graph. Programs include metacomputation operators for
reference and revision that reuse dataflow fragments from previous turns. Our
graph-based state enables the expression and manipulation of complex user
intents, and explicit metacomputation makes these intents easier for learned
models to predict. We introduce a new dataset, SMCalFlow, featuring complex
dialogues about events, weather, places, and people. Experiments show that
dataflow graphs and metacomputation substantially improve representability and
predictability in these natural dialogues. Additional experiments on the
MultiWOZ dataset show that our dataflow representation enables an otherwise
off-the-shelf sequence-to-sequence model to match the best existing
task-specific state tracking model. The SMCalFlow dataset and code for
replicating experiments are available at
this https URL

本研究提出了一种基于数据流图表示对话状态并引入元计算符以复用过去状态的方法，并针对此开发了 SMCalFlow 数据集以进行有关事件、天气、地点和人物等问题的复杂对话，实验证明基于数据流图和元计算的方法可显著提高此类自然对话的可表达性和可预测性，同时将其与现有的任务领域状态跟踪模型进行了比较。