  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.
