# A Joint Model for Question Answering and Question Generation

arXiv: https://arxiv.org/abs/1706.01450 · HTML: https://mlsanity.com/paper/1706.01450/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2017.

## Built on by (most cited first, 13 of 13)

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- [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://mlsanity.com/paper/2109.03423/) (2021)
- [Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval](https://mlsanity.com/paper/2104.08801/) (2021)
- [OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach](https://mlsanity.com/paper/2102.12128/) (2021)
- [Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data](https://mlsanity.com/paper/2109.05179/) (2021)
- [Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction](https://mlsanity.com/paper/2112.03849/) (2021)
- [On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation](https://mlsanity.com/paper/2203.04464/) (2022)
- [UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization](https://mlsanity.com/paper/2306.06851/) (2023)
- [Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning](https://mlsanity.com/paper/2204.11718/) (2022)
- [Using Implicit Feedback to Improve Question Generation](https://mlsanity.com/paper/2304.13664/) (2023)
- [DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering](https://mlsanity.com/paper/2412.16694/) (2024)

## Abstract (arXiv)

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model's novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.