Unsupervised Domain Adaptation on Reading Comprehension
Table 4: EM results of CASe ablation test on 4 dataset pairs.
['[EMPTY]', '[BOLD] C→S', '[BOLD] D→C', '[BOLD] C→N', '[BOLD] S→Co']
We do ablation test on 4 domain adaptation dataset pairs, which are CNN to SQuAD (C→S), DailyMail to CNN (D→C), CNN to NewsQA (C→N) and SQuAD to CoQA (S→Co), including adaptation between datasets with same/different question forms and/or corpora. It is observed that self-training plays the most important role under all configurations. Performance drops without discriminator conditioning on output or whole adversarial learning. Batch normalization has slight effect, removing it promotes the results under two configurations while it has opposite effect under others.