Symbolic Association Using Parallel Multilayer Perceptron

The goal of our paper is to learn the association and the semantic grounding of two sensory input signals that represent the same semantic concept. The input signals can be or cannot be the same modality. This task is inspired by infants learning. We propose a novel framework that has two symbolic Multilayer Perceptron (MLP) in parallel. Furthermore, both networks learn to ground semantic concepts and the same coding scheme for all semantic concepts in both networks. In addition, the training rule follows EM-approach. In contrast, the traditional setup of association task pre-defined the coding scheme before training. We have tested our model in two cases: mono-and multi-modal. Our model achieves similar accuracy association to MLPs with pre-defined coding schemes.