While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.

翻译：虽然自然语言理解(NLU)正在迅速发展,但今天的技术在基本方式上不同于类似人类的语言理解,特别是在低效率、可解释性和概括性方面,这项工作提出了一种基于隐含认知语言(ECL)原则的代言和学习方法。根据ECL,自然语言本质上是可以执行的(如编程语言),其驱动力是心理模拟和隐喻图解结构的等级构成和通过隐含的互动所学的系统。本立场文件认为,使用隐喻推论和模拟进行基建将极大地有利于NLU系统,并提出了一个系统架构以及实现这一愿景的路线图。