NUDGING NEURAL CONVERSATIONAL MODEL WITH DOMAIN KNOWLEDGE

摘要

Examples of the present disclosure describe systems and methods utilize domain knowledge to influence a selection of a candidate action template in a neural conversation model. More specifically, natural language rules may be provided to a natural language rule inferencer to bias a selection of a candidate action template. In some instances, the natural language rules may include a user input and a system action. In other instances, the natural language rules may include a previous system action and a next system action. A biasing vector may then influence a selection of a candidate action template of a set of candidate action templates to determine a most relevant candidate action template based on the natural language rules, the candidate action templates, and the user utterance or other system input.