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 https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines.
翻译:我们描述一种以任务为导向的对话方法,其中对话状态以数据流图的形式呈现。一个对话代理器将每个用户的言论映射为扩展此图的程序。程序包括用于参考和修改的元化计算操作器,以便重新使用先前转弯的数据流碎片。基于图形的状态使得复杂的用户意图的表达和操作得以进行,而明确的元化使这些意图更易于让学习到的模型预测。我们引入一个新的数据集,即SMCalFlow,以关于事件、天气、地点和人的复杂对话为主。实验显示,数据流图和元化转换大大改进了这些自然对话的可代表性和可预测性。关于多功能区数据集的其他实验显示,我们的数据流显示,我们的数据流模式可以与现有最佳的任务特定状态跟踪模型相匹配。SMCalFlow数据集和复制实验代码可在https://www.microistorf.com/en-us/reearch/procation-plow-broad-dialog-se-mantical-chmas.