Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality (P18-2)
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| Challenge: | Recurrent neural tensor networks (RNNs) increase capacity by augmenting the size of the hidden layer, with significant increase in computational cost. |
| Approach: | They propose restricted recurrent neural tensor networks (r-RNTNs) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights . |
| Outcome: | The proposed model outperforms unrestricted RNTNs using only a small fraction of the parameters of unrestrained RNNNs. |
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