Robust and Scalable Differentiable Neural Computerfor Question Answering
Table 2: The validation and test accuracy (%) of the rsDNC/BrsDNC and others on CNN dataset.
['Model', 'valid', 'test']
S6SS2SSS0Px3 Results Both models achieve passable results compared to task-specific state-of-the-art models. But when focusing on the fact that our model is trained without any adaption to the task like sentence representations or word-windows, without hyperparameter tuning or any other optimization it is a satisfactory result. The bidirectional model outperforms the unidirectional rsDNC, but only on the test set. The training time of our implementation is 14/16 hours per epoch on the rsDNC/BrsDNC. A DNC/BDNC requires up to 35/41h per epoch. The overall training time is 7 epochs on average. The memory savings of the CBMU result in 4 training days instead of 12. This shows the model’s adaptability to perform other tasks without any task-specific adjustment and its scalability due to a drastic reduction of training time.