Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks. In this article, inspired by human communication, we propose a novel stochastic model of System 1 semantics-native communication (SNC) for generic tasks, where a speaker has an intention of referring to an entity, extracts the semantics, and communicates its symbolic representation to a target listener. To further reach its full potential, we additionally infuse contextual reasoning into SNC such that the speaker locally and iteratively self-communicates with a virtual agent built on the physical listener's unique way of coding its semantics, i.e., communication context. The resultant System 2 SNC allows the speaker to extract the most effective semantics for its listener. Leveraging the proposed stochastic model, we show that the reliability of System 2 SNC increases with the number of meaningful concepts, and derive the expected semantic representation (SR) bit length which quantifies the extracted effective semantics. It is also shown that System 2 SNC significantly reduces the SR length without compromising communication reliability.

翻译：受后香农通信领域巨大兴趣的推动，近期研究表明，利用语义可以显著提升诸多任务的通信效率。本文受人类通信启发，针对通用任务提出了一种新颖的System 1语义原生通信（SNC）随机模型：说话者基于指称实体的意图提取语义，并将其符号化表征传输至目标听者。为进一步挖掘潜力，我们在SNC中融入情境推理，使说话者能够基于物理听者独特的语义编码方式（即通信情境），通过虚拟智能体进行局部迭代式自我通信。由此产生的System 2 SNC使说话者能为其听者提取最有效的语义。基于所提出的随机模型，我们证明System 2 SNC的可靠性随有意义概念数量的增加而提升，并推导出量化有效语义提取程度的预期语义表征（SR）比特长度。研究表明，System 2 SNC在保持通信可靠性的前提下显著缩短了语义表征长度。