Tracking the World State with Recurrent Entity Networks
Table 2: Results on bAbI Tasks with 10k training samples.
['Task', 'NTM', 'D-NTM', 'MemN2N', 'DNC', 'DMN+', 'EntNet']
Our model is able to solve all the tasks, outperforming the other models in terms of both the number of solved tasks and the average error. We report results for the mean performance across different random seeds (20 for the DNC, 5 for the EntNet), as well as the performance for the single best seed (measured by validation error). The DNC results for mean performance were taken from the appendix of Graves et al. The DNC has better performance in terms of the best seed, but also exhibits high variation across seeds, indicating that many different runs are required to achieve good performance. The EntNet exhibits less variation across runs and is able to solve more tasks consistently.