This paper proposes a new architecture - Attentive Tensor Product Learning
(ATPL) - to represent grammatical structures in deep learning models. ATPL is a
new architecture to bridge this gap by exploiting Tensor Product
Representations (TPR), a structured neural-symbolic model developed in
cognitive science, aiming to integrate deep learning with explicit language
structures and rules. The key ideas of ATPL are: 1) unsupervised learning of
role-unbinding vectors of words via TPR-based deep neural network; 2) employing
attention modules to compute TPR; and 3) integration of TPR with typical deep
learning architectures including Long Short-Term Memory (LSTM) and Feedforward
Neural Network (FFNN). The novelty of our approach lies in its ability to
extract the grammatical structure of a sentence by using role-unbinding
vectors, which are obtained in an unsupervised manner. This ATPL approach is
applied to 1) image captioning, 2) part of speech (POS) tagging, and 3)
constituency parsing of a sentence. Experimental results demonstrate the
effectiveness of the proposed approach