@inproceedings{zhu-etal-2019-learning,
 abstract = {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.},
 address = {Florence, Italy},
 author = {Zhu, Haichao  and
Dong, Li  and
Wei, Furu  and
Wang, Wenhui  and
Qin, Bing  and
Liu, Ting},
 booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
 doi = {10.18653/v1/P19-1415},
 month = {July},
 pages = {4238--4248},
 publisher = {Association for Computational Linguistics},
 title = {Learning to Ask Unanswerable Questions for Machine Reading Comprehension},
 url = {https://www.aclweb.org/anthology/P19-1415},
 year = {2019}
}