Dual Co-Matching Network for Multi-choice Reading Comprehension
Table 2: Experiment results on RACE test set. † means it is statistically significant to the models ablating either the bidirectional matching or gated mechanism. ∗ indicates ensemble model. DMNbase uses BERTbase as encoder and DMNlarge uses BERTlarge as encoder.
['[BOLD] Model', 'RACE-M', 'RACE-H', 'RACE']
Turkers is the performance of Amazon Turkers on a random subset of the RACE test set. Ceiling is the percentage of unambiguous questions in the test set. Comparison results show that our model is powerful and even the sing model outperforms all baselines and achieves new state-of-the-art accuracy. Our ensemble model further improves the performance for 1.5%. In this work, we mainly focus on show two core model improvements, (1) the bidirectional matching strategy and (2) the gated mechanism. We observe 1.5% performance decrease by only using unidirectional matching. In detail, we only build answer-aware passage representation without considering passage-aware answer representation when modeling the passage-answer sequence pair relationship (i.e., only use Sp as the matching representation without Sa in Eq. The ablation experiments on ROCStories dataset further prove above descriptions.