Learning to Ask Unanswerable Questions for Machine Reading Comprehension
Haichao Zhu, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, Ting Liu · 2019
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.