Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.18653/v1/N18-1114 | NAACL-HLT |
Field | DocType | Volume |
Tensor product,Grammatical category,Computer science,Network architecture,Artificial intelligence,Natural language processing,Deep learning,Artificial neural network,Distributed representation,Encoding (memory),Computation | Conference | 1 |
Citations | PageRank | References |
2 | 0.35 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiuyuan Huang | 1 | 176 | 17.66 |
| Paul Smolensky | 2 | 215 | 93.76 |
| Xiaodong He | 3 | 3858 | 190.28 |
| Deng, Li | 4 | 9691 | 728.14 |
| Dapeng Oliver Wu | 5 | 4 | 1.43 |