Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
Table 3: The performances of the vanilla and memory-augmented recurrent models on D3. In 32 out of 100 trials, the MARNNs with 8 hidden units and one-dimensional memory achieved over 99% accuracy on the test sets. However, increasing the dimensional of the memory for our MARNNs further improved our results.
['[BOLD] Models', '[BOLD] Training Set  [BOLD] Min', '[BOLD] Training Set  [BOLD] Max', '[BOLD] Training Set  [BOLD] Med', '[BOLD] Training Set  [BOLD] Mean', '[BOLD] Test Set  [BOLD] Min', '[BOLD] Test Set  [BOLD] Max', '[BOLD] Test Set  [BOLD] Med', '[BOLD] Test Set  [BOLD] Mean']
Following the Stack-RNNs, the Stack-LSTMs and Baby-NTMs, on average, achieved around 50% and 37% accuracy on the test set, respectively.