Dialogue as Dataflow: A new approach to conversational AI
By the Semantic Machines research team
“Easier said than done.” These four words reflect the promise of conversational AI. It takes just seconds to ask When are Megan and I both free? but much longer to find out manually from a calendar. Indeed, almost everything we do with technology can feel like a long path to a short goal. At Microsoft Semantic Machines, we’re working to bridge this gap—to build conversational AI experiences where you can focus on saying what you want and the system will worry about how to get it done. You should be able to speak as you speak to a friend: naturally, contextually, and collaboratively.
A truly powerful conversational AI needs to do more than deeply understand language. To be contextual, flexible, and robust, the AI must also deeply understand actions—most goals involve multiple steps and multiple sources of information. Representing goals, actions, and dialogue states is one of the central challenges in conversational AI systems. Our new paper in Transactions of the Association for Computational Linguistics (TACL), titled “Task-Oriented Dialogue as Dataflow Synthesis,” describes a new representation and modeling framework that interprets dialogues as dataflow graphs, enabling conversations about