  Natural language communication is an intricate and complex process. The
speaker usually begins with an intention and motivation of what is to be
communicated, and what effects are expected from the communication, while
taking into consideration the listener's mental model to concoct an appropriate
sentence. The listener likewise has to interpret what the speaker means, and
respond accordingly, also with the speaker's mental state in mind. To do this
successfully, conceptual, motivational, and affective processes have to be
represented appropriately to drive the language generation and understanding
processes. Language processing has succeeded well with the big data approach in
applications such as chatbots and machine translation. However, in human-robot
collaborative social communication and in using natural language for delivering
precise instructions to robots, a deeper representation of the conceptual,
motivational, and affective processes is needed. This paper capitalizes on the
UGALRS (Unified General Autonomous and Language Reasoning System) framework and
the CD+ (Conceptual Representation Plus) representational scheme to illustrate
how social communication through language is supported by a knowledge
representational scheme that handles conceptual, motivational, and affective
processes in a deep and general way. Though a small set of concepts,
motivations, and emotions is treated in this paper, its main contribution is in
articulating a general framework of knowledge representation and processing to
link these aspects together in serving the purpose of natural language
communication for an intelligent system.
