Iteratively updating parameters for dialog states

A method for training a plurality of components of an interaction system, the method comprising:

• collecting a training data set including one or more ordered pluralities of events, and, for each ordered plurality of events, a corresponding ordered plurality of output actions, wherein collecting the training data set includes:
• recognizing an exemplary linguistic event during natural language interaction with a human user;
• presenting the exemplary linguistic event to a human trainer;
• and
• receiving, from the human trainer, an exemplary output action to associate with the exemplary linguistic event;
• and
• for an exemplary ordered plurality of events and a corresponding exemplary ordered plurality of output actions:
• generating an ordered plurality of output actions based on the exemplary ordered plurality of events and current parameter values for trainable parameters of the interaction system;
• comparing the generated ordered plurality of output actions to the exemplary ordered plurality of output actions;
• and
• updating the trainable parameters of the interaction system based on the comparison.

A method for training a plurality of components of an interaction system, the method comprising:

• collecting a training data set including one or more ordered pluralities of events, and, for each ordered plurality of events, a corresponding ordered plurality of output actions;
• for an exemplary ordered plurality of events and a corresponding exemplary ordered plurality of output actions:
• generating an ordered plurality of output actions based on the exemplary ordered plurality of events and current parameter values for trainable parameters of the interaction system;
• assessing a comparison score based on comparing the generated ordered plurality of output actions to the exemplary ordered plurality of output actions;
• and
• updating the trainable parameters of the interaction system based on the assessed comparison score to improve a likelihood of generating a correct ordered plurality of output actions with regard to a future ordered plurality of events.