Much of our NLP work originates from DISCERN, a large-scale natural language processing system implemented entirely at the subsymbolic level. In DISCERN, distributed neural network models of parsing, generating, reasoning, lexical processing, and episodic memory are integrated into a single system that learns to read, paraphrase, and answer questions about stereotypical narratives. In this approach, subsymbolic networks are not only plausible models of isolated cognitive phenomena, but also serve as building blocks for large-scale artificial intelligence systems.