Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
Table 3: Ablation studies of data augmentation on WIQA and QuaRel development dataset.
['metric', 'WIQA acc', 'WIQA  [ITALIC] v (%)', 'QuaRel acc', 'QuaRel  [ITALIC] v (%)']
Ablation study DA and Reg each improves the baselines, and the combination performs the best on WIQA and QuaRel. Importantly, DA (standard) does not give notable improvement over the baseline model both in accuracy and consistency, which suggests that logic-guided augmentation gives additional inductive bias for consistent QA beyond amplifying the number of train data. As WIQA consists of some transitive or symmetric examples, we also report the performance with Reg only on WIQA. The performance improvements is smaller, demonstrating the importance of combining with DA.