# Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data

arXiv: https://arxiv.org/abs/2109.05179 · HTML: https://mlsanity.com/paper/2109.05179/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [GLGE: A New General Language Generation Evaluation Benchmark](https://mlsanity.com/paper/2011.11928/) (2020)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [Question Answering and Question Generation as Dual Tasks](https://mlsanity.com/paper/1706.02027/) (2017)
- [Generating Distractors for Reading Comprehension Questions from Real Examinations](https://mlsanity.com/paper/1809.02768/) (2018)
- [Generating Question-Answer Hierarchies](https://mlsanity.com/paper/1906.02622/) (2019)
- [Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space](https://mlsanity.com/paper/2010.01475/) (2020)
- [Evaluating Rewards for Question Generation Models](https://mlsanity.com/paper/1902.11049/) (2019)
- [EQG-RACE: Examination-Type Question Generation](https://mlsanity.com/paper/2012.06106/) (2020)
- [A Joint Model for Question Answering and Question Generation](https://mlsanity.com/paper/1706.01450/) (2017)
- [Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension](https://mlsanity.com/paper/1706.09789/) (2017)
- [OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach](https://mlsanity.com/paper/2102.12128/) (2021)
- [Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model](https://mlsanity.com/paper/1910.08832/) (2019)

## Built on by (most cited first, 4 of 4)

- [HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization](https://mlsanity.com/paper/2203.10741/) (2022)
- [Diversify Question Generation with Retrieval-Augmented Style Transfer](https://mlsanity.com/paper/2310.14503/) (2023)
- [From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams](https://mlsanity.com/paper/2206.05442/) (2022)
- [A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering](https://mlsanity.com/paper/2211.16971/) (2022)

## Abstract (arXiv)

Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field. This paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE. To capture the important information of the input passage we first automatically generate(rather than extracting) keyphrases, thus this task is reduced to keyphrase-question-answer triplet joint generation. Accordingly, we propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively, and then apply the generated question and keyphrases to guide the generation of answers. To establish a solid benchmark, we build our model on the strong generative pre-training model. Experimental results show that our model makes great breakthroughs in the question-answer pair generation task. Moreover, we make a comprehensive analysis on our model, suggesting new directions for this challenging task.