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Symbolic Association Learning inspired by the Symbol Grounding Problem

Federico Raue; Marcus Liwicki; Andreas Dengel
In: Thomas Villmann; Frank-Michael Schleif (Hrsg.). Machine Learning Reports 04/2016. Workshop New Challenges in Neural Computation (NC2-2016), German Conference of Pattern Recognition, located at GCPR, September 12, Hannover, Germany, Pages 40-47, online, 2016.


In this work, we present a novel model for a cognitive association task where two visual sequences represent different instances of the same semantic sequence. Also, the model learns the binding between abstract concepts and vectorial representations (e.g., 1-of-K scheme). In this case, the output vector of a network are used as symbolic features, and the network learns to ground the abstract concepts to them. This task is inspired by the Symbol Grounding Problem. Our model uses one Long Short-Term Memory (LSTM) with an EM-training rule. One important feature of the training is to use one of the two sequences as a target of the other sequence for updating the LSTM network, and vice versa. Our architecture is based on a recent model that uses two LSTM networks for this association task. We compare our model using a generated dataset from MNIST. The presented model reaches similar results against the model with two LSTM networks. Also, our model is compared to a trained LSTM using only one sequence with a predefined binding of the abstract concepts, and the performance is also similar