When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols. Good task performance is often considered sufficient evidence that meaningful communication is taking place, but existing empirical results show that communication strategies induced by common objectives can be counterintuitive whilst solving the task nearly perfectly. In this work, we identify a goal-agnostic prerequisite to meaningful communication, which we term semantic consistency, based on the idea that messages should have similar meanings across instances. We provide a formal definition for this idea, and use it to compare the two most common objectives in the field of emergent communication: discrimination and reconstruction. We prove, under mild assumptions, that semantically inconsistent communication protocols can be optimal solutions to the discrimination task, but not to reconstruction. We further show that the reconstruction objective encourages a stricter property, spatial meaningfulness, which also accounts for the distance between messages. Experiments with emergent communication games validate our theoretical results. These findings demonstrate an inherent advantage of distance-based communication goals, and contextualize previous empirical discoveries.

翻译：当人工智能体通过通信信道联合训练以执行协作任务时，它们会发展出不透明的目标导向通信协议。良好的任务性能通常被视为发生有意义通信的充分证据，但现有实证结果表明，常见目标所诱导的通信策略可能在近乎完美解决任务的同时显得反直觉。本研究识别了有意义通信的一个与目标无关的先决条件，我们称之为语义一致性，其核心思想是消息在不同实例中应具有相似含义。我们为此概念提供了形式化定义，并以此比较涌现式通信领域两种最常见的训练目标：判别任务与重构任务。我们在温和假设条件下证明，语义不一致的通信协议可能成为判别任务的最优解，但不可能成为重构任务的最优解。我们进一步证明，重构目标会促进更严格的空间意义性，该性质同时考虑了消息之间的距离关系。通过涌现式通信游戏的实验验证了我们的理论结果。这些发现揭示了基于距离的通信目标具有内在优势，并为先前的实证发现提供了理论背景。