Grammar with Gradient Symbol Structures

Lecture by Paul Smolensky, Sapir Professor, at the Max Palevsky Cinema in Ida Noyes, at 6pm. Reception to follow at 7:30pm at the Cloister Club, also in Ida Noyes.

How can grammatical theory contribute to, and benefit from, advances in psycholinguistics, computational linguistics, and cognitive neuroscience? I will propose a novel computational framework for grammar — its structure, use and acquisition — providing formal characterizations of grammar at both an abstract, functional level and a neural-network level. The most recent developments in this framework involve linguistic representations in which partially-active symbols occupy blends of positions within discrete structures such as trees. Application of such Gradient Symbolic Computation (GSC) to grammatical competence theory tests a general hypothesis: theoretical disputes about whether phenomenon X should be analyzed using structure A or structure B persist because the correct analysis is in fact a blend of those two structures. Application of GSC to language production and comprehension tests whether this type of computation can successfully unify discrete, structure-based theories of competence with gradient, activation-based theories of performance. The general architecture raises important questions about current corpus-based computational linguistic systems that appear to achieve remarkable linguistic performance using unstructured numerical vectors as internal representations (e.g., of semantics), and deep neural networks for learning those representations.