Parallel sequence classification using recurrent neural networks and alignment

Federico Raue, Wonmin Byeon, Thomas M. Breuel, Marcus Liwicki

Abstract: The aim of this work is to investigate Long Short-Term Memory (LSTM) for finding the semantic associations between two parallel text lines of different instances of the same class sequence. In this work, we propose a new model called class-less classifier, which is cognitive motivated by a simplified version of the infants learning. The presented model not only learns the semantic association but also learns the relation between the labels and the classes. In addition, our model uses two parallel class-less LSTM networks and the learning rule is based on the alignment of both networks. For testing purposes, a parallel sequence dataset is generated based on MNIST dataset, which is a standard dataset for handwritten digit recognition. The results of our model were similar to the standard LSTM.