Enabling ´Question Answering´ in the MBAT Vector Symbolic Architecture by Exploiting Orthogonal Random Matrices

Vector Symbolic Architectures (VSA) are methods designed to enable distributed representation and manipulation of semantically-structured information, such as natural languages. Recently, a new VSA based on multiplication of distributed vectors by random matrices was proposed, this is known as Matrix-Binding-of-Additive-Terms (MBAT). We propose an enhancement that introduces an important additional feature to MBAT: the ability to ´unbind´ symbols. We show that our method, which exploits the inherent properties of orthogonal matrices, imparts MBAT with the ´question answering´ ability found in other VSAs. We compare our results with another popular VSA that was recently demonstrated to have high utility in brain-inspired machine learning applications.