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arXiv · 1906.06045

Learning to Ask Unanswerable Questions for Machine Reading Comprehension

Haichao Zhu·Li Dong·Furu Wei·Wenhui Wang·Bing Qin·Ting Liu
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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.

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Haichao Zhu, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, Ting Liu. 2019-06-14. Learning to Ask Unanswerable Questions for Machine Reading Comprehension. https://arxiv.org/abs/1906.06045

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