  Machine reading comprehension with unanswerable questions is a challenging
task. In this work, we propose a data augmentation technique by automatically
generating relevant unanswerable questions according to an answerable question
paired with its corresponding paragraph that contains the answer. We introduce
a pair-to-sequence model for unanswerable question generation, which
effectively captures the interactions between the question and the paragraph.
We also present a way to construct training data for our question generation
models by leveraging the existing reading comprehension dataset. Experimental
results show that the pair-to-sequence model performs consistently better
compared with the sequence-to-sequence baseline. We further use the
automatically generated unanswerable questions as a means of data augmentation
on the SQuAD 2.0 dataset, yielding 1.9 absolute F1 improvement with BERT-base
model and 1.7 absolute F1 improvement with BERT-large model.
