Ask Me Anything:Dynamic Memory Networks for Natural Language Processing
Table 1: Test accuracies on the bAbI dataset. MemNN numbers taken from Weston et al. (Weston et al., 2015a). The DMN passes (accuracy > 95%) 18 tasks, whereas the MemNN passes 16.
['Task', 'MemNN', 'DMN']
The DMN does worse than the Memory Network, which we refer to from here on as MemNN, on tasks 2 and 3, both tasks with long input sequences. We suspect that this is due to the recurrent input sequence model having trouble modeling very long inputs. The MemNN does not suffer from this problem as it views each sentence separately. The power of the episodic memory module is evident in tasks 7 and 8, where the DMN significantly outperforms the MemNN. Both tasks require the model to iteratively retrieve facts and store them in a representation that slowly incorporates more of the relevant information of the input sequence. Both models do poorly on tasks 17 and 19, though the MemNN does better. We suspect this is due to the MemNN using n-gram vectors and sequence position features.