Multi-range Reasoning for Machine Comprehension
Table 1: Comparison against other published models on RACE dataset lai2017race . Competitor result are reported from lai2017race ; xu2017towards . Best result for each category (single and ensemble) is in boldface. Last column reports estimated training time per epoch and total time for convergence. ∗ estimated values that we obtain from asking the authors.
['Model', 'RACE-M', 'RACE-H', 'RACE', 'Time']
Our proposed MRU model achieves the best result for both single models and ensemble models. We outperform highly complex models such as DFN. We also pull ahead of other recent baselines such as ElimiNet and GA by at least 5%. The best single model score from RACE-H and RACE-M alternates between Sim. MRU and MRU. Overall, there is a 6% improvement on the RACE-H dataset and 1.8% improvement on the RACE-M dataset. Our Sim. MRU model also runs at 4 min per iteration, which is dramatically faster and simpler than DFN or other recurrent models. We believe that this finding highlights the importance of designing strong and fast baselines for the task at hand.