Natural language generation based on user speech style

A method of generating a natural language generation (NLG) output, wherein the method comprises:

• receiving speech signals from a user at a microphone of a client device;
• identifying a user or a likely user that transmitted the speech signals;
• determining a communication goal and at least one inputted communication value based on the received speech signals;
• determining whether to use a static NLG template or dynamic NLG template for use in generating an NLG output, wherein the determination of whether to use a static NLG template or dynamic NLG template is made using a neural network NLG template selection process, wherein the neural network NLG template selection process uses an artificial neural network to resolve a set of inputs to select whether to use a static NLG template or dynamic NLG template for use in generating an NLG output, and wherein the set of inputs include the communication goal, the inputted communication values, and either a user history that is associated with the user or a user profile that is associated with the user;
• where it is determined to use a static NLG template, then selecting a static NLG template;
• when it is determined to use a dynamic NLG template, then generating the dynamic NLG template;
• and
• generating the NLG output using the selected NLG template.