  We present a new approach to the design of deep networks for natural language
processing (NLP), based on the general technique of Tensor Product
Representations (TPRs) for encoding and processing symbol structures in
distributed neural networks. A network architecture --- the Tensor Product
Generation Network (TPGN) --- is proposed which is capable in principle of
carrying out TPR computation, but which uses unconstrained deep learning to
design its internal representations. Instantiated in a model for image-caption
generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset.
The TPR-capable structure enables interpretation of internal representations
and operations, which prove to contain considerable grammatical content. Our
caption-generation model can be interpreted as generating sequences of
grammatical categories and retrieving words by their categories from a plan
encoded as a distributed representation.
