Training a user-system dialog in a task-oriented dialog system

A system comprising:

• at least one processor;
• and
• at least one memory storing computer-executable instructions that when executed by the at least one processor cause the system to:
• receive a first dialog graph comprising a plurality of nodes and at least one edge connecting two nodes of the plurality of nodes, wherein the first dialog graph represents a dialog flow including each of the plurality of nodes defining an action associated with the corresponding node and the at least one edge defining a condition linking the two nodes, wherein a first path connects at least a first preceding node to at least a first subsequent node of the plurality of nodes through one or more edges, and wherein a second path connects at least the first preceding node to at least a second subsequent node of the plurality of nodes;
• translate the first path of the first dialog graph into a first text-based dialog and the second path of the first dialog graph into a second text-based dialog, wherein the first text-based dialog and the second text-based dialog are in a data format adapted for training a neural network and represent the dialog flow of the first dialog graph;
• train the neural network based at least on the first text-based dialog and the second text-based dialog as training data;
• deploy the neural network to generate a log dialog of deployed user-system interactions, wherein at least one deployed dialog is included in the log dialog;
• identify an exception associated with the deployed dialog in the log dialog;
• convert at least a portion of the log dialog into a second dialog graph;
• receive an edit directly to the log dialog via an interactive dialog editor tool to mitigate the exception and to create a corrected dialog;
• retrain the neural network based on the plurality of corrected dialog;
• update the second dialog graph associated with the log dialog based on the corrected dialog.

A computer storage medium storing computer-executable instructions that when executed a processor cause a computer system to:

• receive a first dialog graph comprising a plurality of nodes and at least one edge connecting two nodes of the plurality of nodes, wherein the first dialog graph represents a dialog flow including each of the plurality of nodes defining an action associated with the corresponding node and the at least one edge defining a condition linking the two nodes, wherein a first path connects at least a first preceding node to at least a first subsequent node of the plurality of nodes through one or more edges, and wherein a second path connects at least the first preceding node to at least a second subsequent node of the plurality of nodes;
• convert the first path of the first dialog graph into a first text-based dialog and the second path of the first dialog graph into a second text-based dialog, wherein the first text-based dialog and the second text-based dialog are in a data format adapted for training a machine learning system and represent the dialog flow of the first dialog graph;
• train the machine learning system based at least on the first text-based dialog and the second text-based dialog as training data;
• receive a log dialog, wherein the log dialog is generated based on executing the machine learning system to deploy a dialog, wherein a conversation thread associated with the deployed dialog is included in the log dialog;
• cause display of the log dialog in a machine-teaching tool of a user interface;
• convert at least a portion of the log dialog into a second dialog graph;
• provide an indication of an exception in the log dialog, wherein the exception is associated with the deployed dialog;
• receive an edit directly to the conversation thread of the deployed dialog in the log dialog via an interactive dialog editor tool to mitigate the exception;
• retrain the machine learning system based on the edited log dialog;
• update the second dialog graph associated with the log dialog based on a corrected dialog.