Adjusting a verbosity of a conversation turn

A computer program product for adjusting a verbosity of a conversation turn, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:

• identifying, by the processor, current choices with different verbosity levels for a current turn in a conversation;
• normalizing, by the processor, multi-dimensional verbosity vectors for each of the current choices to obtain a normalized value for each of the current choices;
• determining, by the processor, a state definition for the current turn in the conversation, utilizing the normalized values for each of the current choices;
• providing, by the processor, the state definition for the current turn in the conversation and the normalized values for each of the current choices to a trained reinforcement learning module;
• receiving, from the trained reinforcement learning module by the processor, a score associated with each of the current choices for the current turn in the conversation;
• and
• selecting, by the processor, one of the current choices to be entered for the current turn in the conversation, based on the score associated with each of the current choices for the current turn in the conversation.

A system, comprising:

• a processor;
• and
• logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
• identify current choices with different verbosity levels for a current turn in a conversation;
• normalize multi-dimensional verbosity vectors for each of the current choices to obtain a normalized value for each of the current choices;
• determine a state definition for the current turn in the conversation, utilizing the normalized values for each of the current choices;
• provide the state definition for the current turn in the conversation and the normalized values for each of the current choices to a trained reinforcement learning module;
• receive, from the trained reinforcement learning module, a score associated with each of the current choices for the current turn in the conversation;
• and
• select one of the current choices to be entered for the current turn in the conversation, based on the score associated with each of the current choices for the current turn in the conversation.