Language is crucial for human intelligence, but what exactly is its role? We
take language to be a part of a system for understanding and communicating
about situations. The human ability to understand and communicate about
situations emerges gradually from experience and depends on domain-general
principles of biological neural networks: connection-based learning,
distributed representation, and context-sensitive, mutual constraint
satisfaction-based processing. Current artificial language processing systems
rely on the same domain general principles, embodied in artificial neural
networks. Indeed, recent progress in this field depends on \emph{query-based
attention}, which extends the ability of these systems to exploit context and
has contributed to remarkable breakthroughs. Nevertheless, most current models
focus exclusively on language-internal tasks, limiting their ability to perform
tasks that depend on understanding situations. These systems also lack memory
for the contents of prior situations outside of a fixed contextual span. We
describe the organization of the brain's distributed understanding system,
which includes a fast learning system that addresses the memory problem. We
sketch a framework for future models of understanding drawing equally on
cognitive neuroscience and artificial intelligence and exploiting query-based
attention. We highlight relevant current directions and consider further
developments needed to fully capture human-level language understanding in a
computational system.

语言是理解和交流情境的关键，而语言处理系统使用类似人脑的神经网络以及查询注意力等方法来提高其能力，但当前的模型主要集中在内部语言任务上，限制其理解情境的能力，因此需要未来的模型应用认知神经科学和人工智能来扩展理解任务。