mlsanityData to 14 Oct 2026

Crowdsourcing Question-Answer Meaning Representations

arXiv 1711.05885 ↗First posted 16 Nov 2017cs.CLcited by 21

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Abstract (arXiv)

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A detailed qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, QA-SRL, and AMR) along with many previously under-resourced ones, including implicit arguments and relations. The QAMR data and annotation code is made publicly available to enable future work on how best to model these complex phenomena.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2017.

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Citing papers per year: 2021: 9; 2022: 5; 2023: 4; 2024: 1; 2025: 1; 2026: 1.

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  1. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  2. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  3. 2021SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoningcited by 77
  4. 2021Comparing Test Sets with Item Response Theorycited by 57
  5. 2021ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphscited by 22
  6. 2021Do Question Answering Modeling Improvements Hold Across Benchmarks?cited by 20
  7. 2021Question Answering Infused Pre-training of General-Purpose Contextualized Representationscited by 19
  8. 2021An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainabilitycited by 16
  9. 2021MetaQA: Combining Expert Agents for Multi-Skill Question Answeringcited by 15
  10. 2023CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Biascited by 14
  11. 2022QASem Parsing: Text-to-text Modeling of QA-based Semanticscited by 12
  12. 2023Localized Symbolic Knowledge Distillation for Visual Commonsense Modelscited by 12
9 more
  1. 2022A Survey on Open Information Extraction from Rule-based Model to Large Language Modelcited by 8
  2. 2022QA Is the New KR: Question-Answer Pairs as Knowledge Basescited by 6
  3. 2021ASQ: Automatically Generating Question-Answer Pairs using AMRscited by 2
  4. 2023Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessmentcited by 2
  5. 2023The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLPcited by 2
  6. 2022Enriching Artificial Intelligence Explanations with Knowledge Fragmentscited by 1
  7. 2025QA-Noun: Representing Nominal Semantics via Natural Language Question-Answer Pairscited by 1
  8. 2026Learning from Child-Directed Speech in Two-Language Scenarios: A French-English Case Studycited by 1
  9. 2024FarFetched: Entity-centric Reasoning and Claim Validation for the Greek Language based on Textually Represented Environmentscited by 0