# Learning to Ask Unanswerable Questions for Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1906.06045 · HTML: https://mlsanity.com/paper/1906.06045/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering](https://mlsanity.com/paper/2101.00391/) (2021)
- [Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering](https://mlsanity.com/paper/2106.11575/) (2021)
- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [Unanswerable Questions about Images and Texts](https://mlsanity.com/paper/2102.06793/) (2021)
- [Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data](https://mlsanity.com/paper/2102.01226/) (2021)
- [IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension](https://mlsanity.com/paper/2210.13778/) (2022)
- [To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://mlsanity.com/paper/2208.01299/) (2022)
- [AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions](https://mlsanity.com/paper/2309.05103/) (2023)

## Abstract (arXiv)

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.