# Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia

arXiv: https://arxiv.org/abs/1805.05942 · HTML: https://mlsanity.com/paper/1805.05942/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 30 of 49)

- [Controlled Text Generation with Natural Language Instructions](https://mlsanity.com/paper/2304.14293/) (2023)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants](https://mlsanity.com/paper/2112.09062/) (2021)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Quiz-Style Question Generation for News Stories](https://mlsanity.com/paper/2102.09094/) (2021)
- [HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization](https://mlsanity.com/paper/2203.10741/) (2022)
- [Improving Biomedical Information Retrieval with Neural Retrievers](https://mlsanity.com/paper/2201.07745/) (2022)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [Question Answering Infused Pre-training of General-Purpose Contextualized Representations](https://mlsanity.com/paper/2106.08190/) (2021)
- [Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation](https://mlsanity.com/paper/2209.11000/) (2022)
- [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://mlsanity.com/paper/2211.01482/) (2022)
- [An Empirical Comparison of LM-based Question and Answer Generation Methods](https://mlsanity.com/paper/2305.17002/) (2023)
- [Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting](https://mlsanity.com/paper/2101.00416/) (2021)
- [Learning to Perturb Word Embeddings for Out-of-distribution QA](https://mlsanity.com/paper/2105.02692/) (2021)
- [Synthetic Question Value Estimation for Domain Adaptation of Question Answering](https://mlsanity.com/paper/2203.08926/) (2022)
- [FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation](https://mlsanity.com/paper/2309.05007/) (2023)
- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [Exploring the Viability of Synthetic Query Generation for Relevance Prediction](https://mlsanity.com/paper/2305.11944/) (2023)
- [Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges](https://mlsanity.com/paper/2309.12426/) (2023)
- [Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap](https://mlsanity.com/paper/2109.11256/) (2021)
- [SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples](https://mlsanity.com/paper/2305.07984/) (2023)
- [Evaluation of Question Generation Needs More References](https://mlsanity.com/paper/2305.16626/) (2023)
- [OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach](https://mlsanity.com/paper/2102.12128/) (2021)
- [s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning](https://mlsanity.com/paper/2110.13640/) (2021)
- [FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering](https://mlsanity.com/paper/2412.07030/) (2024)
- [A Feasibility Study of Answer-Agnostic Question Generation for Education](https://mlsanity.com/paper/2203.08685/) (2022)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data](https://mlsanity.com/paper/2109.05179/) (2021)
- [IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension](https://mlsanity.com/paper/2210.13778/) (2022)
- [Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference](https://mlsanity.com/paper/2306.02379/) (2023)

## Abstract (arXiv)

We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. Compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top-ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We also provide a qualitative analysis for this large-scale generated corpus from Wikipedia.