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A Joint Model for Question Answering and Question Generation

arXiv 1706.01450 ↗First posted 5 Jun 2017cs.CL cs.AI cs.LGcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2017.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 2; 2024: 1.

13 papers.

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  1. 2022StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvementcited by 37
  2. 2021ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collectionscited by 30
  3. 2021Math Word Problem Generation with Mathematical Consistency and Problem Context Constraintscited by 27
  4. 2021It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Bookscited by 23
  5. 2021Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrievalcited by 8
  6. 2021OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approachcited by 7
  7. 2021Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Datacited by 4
  8. 2021Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correctioncited by 1
  9. 2022On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generationcited by 1
  10. 2023UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimizationcited by 1
  11. 2022Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learningcited by 0
  12. 2023Using Implicit Feedback to Improve Question Generationcited by 0
1 more
  1. 2024DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answeringcited by 0