Unsupervised Question Answering by Cloze Translation

@article{Lewis2019UnsupervisedQA,
  title={Unsupervised Question Answering by Cloze Translation},
  author={Patrick Lewis and Ludovic Denoyer and S. Riedel},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.04980}
}
  • Patrick Lewis, Ludovic Denoyer, S. Riedel
  • Published 2019
  • Computer Science
  • ArXiv
  • Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. [...] Key Method To generate such triples, we first sample random context paragraphs from a large corpus of documents and then random noun phrases or named entity mentions from these paragraphs as answers. Next we convert answers in context to "fill-in-the-blank" cloze questions and finally translate them into natural questions.Expand Abstract
    44 Citations

    Figures, Tables, and Topics from this paper

    Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
    • 7
    • Highly Influenced
    • PDF
    Harvesting and Refining Question-Answer Pairs for Unsupervised QA
    • 8
    • Highly Influenced
    • PDF
    Training Question Answering Models From Synthetic Data
    • 12
    • PDF
    Unsupervised Question Decomposition for Question Answering
    • 14
    • PDF
    MLQA: Evaluating Cross-lingual Extractive Question Answering
    • 80
    • PDF
    How Context Affects Language Models' Factual Predictions
    • 16
    • PDF
    Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
    • 4
    • PDF

    References

    SHOWING 1-10 OF 53 REFERENCES
    Semantic Parsing on Freebase from Question-Answer Pairs
    • 1,027
    • Highly Influential
    • PDF
    Natural Questions: A Benchmark for Question Answering Research
    • 321
    • PDF
    Good Question! Statistical Ranking for Question Generation
    • 278
    • Highly Influential
    • PDF
    CoQA: A Conversational Question Answering Challenge
    • 359
    • PDF
    Learning to Ask: Neural Question Generation for Reading Comprehension
    • 281
    • PDF
    Language Models are Unsupervised Multitask Learners
    • 2,472
    • PDF
    Identifying Well-formed Natural Language Questions
    • 15
    • PDF
    Improving Language Understanding by Generative Pre-Training
    • 1,810
    • PDF