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.
@article{arxiv.1906.06045,
title = {Learning to Ask Unanswerable Questions for Machine Reading Comprehension},
author = {Haichao Zhu and Li Dong and Furu Wei and Wenhui Wang and Bing Qin and Ting Liu},
journal= {arXiv preprint arXiv:1906.06045},
year = {2019}
}