Placing language in an integrated understanding system

James McClelland

Abstract:

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. In humans, these abilities emerge gradually
from experience and depend 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 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.