MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
Table 9: Comparison of the test accuracy of the RACE dataset between our approach MMM and the official reports that are from the dataset leaderboard.
['[BOLD] Model', '[BOLD] RACE-M', '[BOLD] RACE-H', '[BOLD] RACE']
Since we found all parts of MMM can work well for the source dataset, we tried to use them to improve the accuracy on RACE. We used four kinds of pre-trained sentence encoders: BERT-Base, BERT-Large, XLNet-Large, and RoBERTa-Large. For each encoder, we listed the official report of scores from the leaderboard. Compared with the baselines, MMM leads to improvements ranging from 0.5% to 3.0% in accuracy. Our best result is obtained by the RoBERTa-Large encoder.