Adversarial Domain Adaptation for Machine Reading Comprehension
Table 2: Performance of AdaMRC compared with baseline models on three datasets, using SAN as the MRC model.
['Method', 'EM/F1']
We observe that the proposed method consistently outperforms SAN and the SynNet+SAN model on all datasets. For example, in the SQuAD→NewsQA setting, where the source-domain dataset is SQuAD and the target-domain dataset is NewsQA, AdaMRC achieves 38.46% and 54.20% in terms of EM and F1 scores, outperforming the pre-trained SAN by 1.78% (EM) and 1.41% (F1), respectively, as well as surpassing SynNet by 3.27% (EM) and 4.59% (F1), respectively. Similar improvements are also observed in NewsQA→SQuAD, SQuAD→MS MARCO and MS MARCO→SQuAD settings, which demonstrates the effectiveness of the proposed model.