  In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and
goal-oriented communication strategies becomes imperative. This integration
will enable sensing, transmission, and processing of exclusively pertinent task
data, ensuring conveyed information possesses understandable, pragmatic
semantic significance, aligning with destination needs and goals. Without
doubt, no communication is error free. Within this context, besides errors
stemming from typical wireless communication dynamics, potential distortions
between transmitter-intended and receiver-interpreted meanings can emerge due
to limitations in semantic processing capabilities, as well as language and
knowledge representation disparities between transmitters and receivers. The
main contribution of this paper is two-fold. First, it proposes and details a
novel mathematical modeling of errors stemming from language mismatches at both
semantic and effectiveness levels. Second, it provides a novel algorithmic
solution to counteract these types of errors which leverages optimal transport
theory. Our numerical results show the potential of the proposed mechanism to
compensate for language mismatches, thereby enhancing the attainability of
reliable communication under noisy communication environments.
