Reduced training for dialog systems using a database

A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:

• receiving a spoken utterance;
• identifying a named entity from the spoken utterance using a machine learning model, wherein the machine learning model has been trained on data extracted from a database that maps a plurality of named entities to respective named entity types based upon columns in the database;
• generating a speech response based upon the identified named entity;
• and
• providing the speech response as output.

A dialog system comprising:

• one or more processors;
• a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:
• receiving a spoken utterance;
• identifying a named entity from the spoken utterance using a machine learning model, wherein the machine learning model has been trained on data extracted from a database that maps a plurality of named entities to respective named entity types based upon columns in the database;
• generating a speech response based upon the identified named entity;
• and
• providing the speech response as output.