# Read + Verify: Machine Reading Comprehension with Unanswerable Questions

arXiv: https://arxiv.org/abs/1808.05759 · HTML: https://mlsanity.com/paper/1808.05759/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

## Built on by (most cited first, 30 of 31)

- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Knowing More About Questions Can Help: Improving Calibration in Question Answering](https://mlsanity.com/paper/2106.01494/) (2021)
- [Can NLI Models Verify QA Systems' Predictions?](https://mlsanity.com/paper/2104.08731/) (2021)
- [Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering](https://mlsanity.com/paper/2101.00391/) (2021)
- [MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization](https://mlsanity.com/paper/2301.12307/) (2023)
- [UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models](https://mlsanity.com/paper/2310.10942/) (2023)
- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [Text Compression-aided Transformer Encoding](https://mlsanity.com/paper/2102.05951/) (2021)
- [Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?](https://mlsanity.com/paper/2212.10784/) (2022)
- [Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework](https://mlsanity.com/paper/2110.08544/) (2021)
- [Text Alignment Is An Efficient Unified Model for Massive NLP Tasks](https://mlsanity.com/paper/2307.02729/) (2023)
- [Unanswerable Questions about Images and Texts](https://mlsanity.com/paper/2102.06793/) (2021)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [Fact-driven Logical Reasoning for Machine Reading Comprehension](https://mlsanity.com/paper/2105.10334/) (2021)
- [MeeQA: Natural Questions in Meeting Transcripts](https://mlsanity.com/paper/2305.08502/) (2023)
- [Towards Confident Machine Reading Comprehension](https://mlsanity.com/paper/2101.07942/) (2021)
- [Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2401.11452/) (2024)
- [YTCommentQA: Video Question Answerability in Instructional Videos](https://mlsanity.com/paper/2401.17343/) (2024)
- [MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension](https://mlsanity.com/paper/2103.04567/) (2021)
- [To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://mlsanity.com/paper/2208.01299/) (2022)
- [Adaptive loose optimization for robust question answering](https://mlsanity.com/paper/2305.03971/) (2023)
- [Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization](https://mlsanity.com/paper/2106.16013/) (2021)
- [FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM Extraction](https://mlsanity.com/paper/2504.05607/) (2025)
- [Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Content](https://mlsanity.com/paper/2509.08304/) (2025)
- [Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features](https://mlsanity.com/paper/2112.05452/) (2021)
- [Deep Understanding based Multi-Document Machine Reading Comprehension](https://mlsanity.com/paper/2204.03494/) (2022)
- [An Understanding-Oriented Robust Machine Reading Comprehension Model](https://mlsanity.com/paper/2207.00187/) (2022)
- [Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2301.00429/) (2023)
- [Teach model to answer questions after comprehending the document](https://mlsanity.com/paper/2307.08931/) (2023)
- [A Lightweight Method to Generate Unanswerable Questions in English](https://mlsanity.com/paper/2310.19403/) (2023)

## Abstract (arXiv)

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional "no-answer" probability to detect unanswerable cases. However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer. To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets. Moreover, we introduce two auxiliary losses to help the reader better handle answer extraction as well as no-answer detection, and investigate three different architectures for the answer verifier. Our experiments on the SQuAD 2.0 dataset show that our system achieves a score of 74.2 F1 on the test set, achieving state-of-the-art results at the time of submission (Aug. 28th, 2018).