mlsanityData to 14 Oct 2026

Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines

arXiv 2205.07387 ↗First posted 15 May 2022cs.CLcited by 5

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Abstract (arXiv)

We present a system called TP3 to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article. TP3 first finetunes pretrained transformers on QAP datasets, then uses a preprocessing pipeline to select appropriate answers, feeds the relevant sentences and the answer to the finetuned transformer to generate candidate QAPs, and finally uses a postprocessing pipeline to filter inadequate QAPs. In particular, using pretrained T5 models as transformers and the SQuAD dataset as the finetruning dataset, we show that TP3 generates satisfactory number of QAPs with high qualities on the Gaokao-EN dataset.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 31 of its 53 references that are in the map. First posted on arXiv 15 May 2022.

Builds on

31 of 53 references.

References · 31

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  11. 2018CoQA: A Conversational Question Answering Challengecited by 683
  12. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 670
19 more
  1. 2015Cyclical Learning Rates for Training Neural Networkscited by 629
  2. 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
  3. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 222
  4. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  5. 2019Simple BERT Models for Relation Extraction and Semantic Role Labelingcited by 154
  6. 2017Semi-supervised sequence tagging with bidirectional language modelscited by 86
  7. 2017Neural Question Generation from Text: A Preliminary Studycited by 78
  8. 2019Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answeringcited by 63
  9. 2019Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generationcited by 57
  10. 2018Towards a Better Metric for Evaluating Question Generation Systemscited by 52
  11. 2018Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examplescited by 34
  12. 2018Improving Neural Question Generation using Answer Separationcited by 30
  13. 2021MixQG: Neural Question Generation with Mixed Answer Typescited by 28
  14. 2021Quiz-Style Question Generation for News Storiescited by 23
  15. 2020Multi-hop Question Generation with Graph Convolutional Networkcited by 20
  16. 2019Question-type Driven Question Generationcited by 16
  17. 2018Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Featurescited by 10
  18. 2019Question Generation by Transformerscited by 10
  19. 2017A Syntactic Approach to Domain-Specific Automatic Question Generationcited by 3

Built on by

Cited by year

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

5 papers.

  1. 2023Evaluating the Performance of Large Language Models on GAOKAO Benchmarkcited by 186
  2. 2022Multilingual Relation Classification via Efficient and Effective Promptingcited by 7
  3. 2023Automatic Generation of Multiple-Choice Questionscited by 2
  4. 2022Tag-Set-Sequence Learning for Generating Question-Answer Pairscited by 1
  5. 2024Constructing Cloze Questions Generativelycited by 0