  In recent research, Tensor Product Representation (TPR) is applied for the
systematic generalization task of deep neural networks by learning the
compositional structure of data. However, such prior works show limited
performance in discovering and representing the symbolic structure from unseen
test data because their decomposition to the structural representations was
incomplete. In this work, we propose an Attention-based Iterative Decomposition
(AID) module designed to enhance the decomposition operations for the
structured representations encoded from the sequential input data with TPR. Our
AID can be easily adapted to any TPR-based model and provides enhanced
systematic decomposition through a competitive attention mechanism between
input features and structured representations. In our experiments, AID shows
effectiveness by significantly improving the performance of TPR-based prior
works on the series of systematic generalization tasks. Moreover, in the
quantitative and qualitative evaluations, AID produces more compositional and
well-bound structural representations than other works.
