Statistical language models for simulating communication sessions

A system, comprising:

• at least one server computer comprising at least one processor and at least one memory, the at least one server computer configured to:
• start a simulated conversation between a user of a first user-type and a simulated user of a second user-type;
• obtain a statistical language model, wherein the statistical language model represents natural language communications of the second user-type and the statistical language model was trained on natural language communications between users of the first user-type and users of the second user-type;
• select a first simulation context from a plurality of available simulation contexts, wherein the first simulation context corresponds to a first difficulty level;
• receive a first natural language communication from the user;
• generate a second natural language communication by processing the first simulation context and the first natural language communication with the statistical language model;
• present the second natural language communication to the user as a simulated communication from the simulated user;
• and
• process natural language communications of the simulated conversation with a conversation scorer model to determine a performance score for the user in the simulated conversation.

One or more non-transitory, computer-readable media comprising computer-executable instructions that, when executed, cause at least one processor to perform actions comprising:

• starting a simulated conversation between a user of a first user-type and a simulated user of a second user-type;
• obtaining a statistical language model, wherein the statistical language model represents natural language communications of the second user-type and the statistical language model was trained on natural language communications between users of the first user-type and users of the second user-type;
• selecting a first simulation context from a plurality of available simulation contexts, wherein the first simulation context corresponds to a first difficulty level;
• receiving a first natural language communication from the user;
• generating a second natural language communication by processing the first simulation context and the first natural language communication with the statistical language model;
• presenting the second natural language communication to the user as a simulated communication from the simulated user;
• and
• processing natural language communications of the simulated conversation with a conversation scorer model to determine a performance score for the user in the simulated conversation.