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.