论文标题
面向任务的对话作为数据流合成
Task-Oriented Dialogue as Dataflow Synthesis
论文作者
论文摘要
我们描述了一种以任务为导向对话的方法，其中对话状态表示为数据流图。对话代理将每个用户的话语映射到扩展此图的程序。程序包括用于参考和修订的元数据运算符，这些操作员从上一轮重复使用数据流片段。我们的基于图的状态实现了复杂用户意图的表达和操纵，并且明确的元数据使学习模型更容易预测这些意图。我们介绍了一个新的数据集，Smcalflow，其中包含有关事件，天气，地点和人员的复杂对话。实验表明，在这些自然对话中，数据流图和元数据大大提高了可表示性和可预测性。多WOZ数据集上的其他实验表明，我们的数据流表示形式可以使原本现成的序列到序列模型匹配最佳现有特定任务特定的状态跟踪模型。用于复制实验的SMCALFLOW数据集和代码可在https://www.microsoft.com/en-us/research/project/project/project/dataflow基于dialogue-semantic-machines上获得。
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.
