Looking at the results on ISNotes, we find that BARQA trained on a small number of in-domain dataset (BASHI) achieves an accuracy of 38.16% on ISNotes, which is better than the model trained on the other two large-scale datasets (SQuAD 1.1 and QuasiBridging).
when using these two datasets to pre-train the model then fine-tuning it with the small in-domain dataset (BASHI), both settings (i.e., SQuAD 1.1 + BASHI and QuasiBridging + BASHI) achieve better results compared to using BASHI as the only training dataset.