We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games. In this model (which we call ReCo, for Regularized Conventions), speakers and listeners search for contextually appropriate utterance--meaning mappings that are both close to game-theoretically optimal conventions and close to a shared, ''default'' semantics. By characterizing pragmatic communication as equilibrium search, we obtain principled sampling algorithms and formal guarantees about the trade-off between communicative success and naturalness. Across several datasets capturing real and idealized human judgments about pragmatic implicatures, ReCo matches or improves upon predictions made by best response and rational speech act models of language understanding.

翻译：我们提出一个语用语言理解模型，其中话语通过搜索信号博弈的正则化均衡来生成和理解。在该模型（称为ReCo，即正则化惯例）中，说话者和听者共同搜索既接近博弈论最优惯例、又接近共享“默认”语义的上下文适宜话语-意义映射。通过将语用沟通描述为均衡搜索，我们获得了原则性的采样算法，以及关于交际成功性与自然性之间权衡的形式化保证。在多个捕捉人类关于语用蕴涵的真实与理想化判断的数据集上，ReCo匹配或优于基于最佳响应与理性言语行为模型的语言理解预测。