A Vector Representation of Fluid Construction Grammar Using Holographic Reduced Representations

The question of how symbol systems can be instantiated in neural network-like computation is still open. Many technical challenges remain and most proposals do not scale up to realistic examples of symbol processing, for example, language understanding or language production. Here we use a top-down approach. We start from Fluid Construction Grammar, a wellworked out framework for language processing that is compatible with recent insights into Construction Grammar and investigate how we could build a neural compiler that automatically translates grammatical constructions and grammatical processing into neural computations. We proceed in two steps. FCG is translated from symbolic processing to numeric processing using a vector symbolic architecture, and this numeric processing is then translated into neural network computation. Our experiments are still in an early stage but already show promise.