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Transforming Question Answering Datasets Into Natural Language Inference Datasets

arXiv 1809.02922 ↗First posted 9 Sep 2018cs.CLcited by 99

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Abstract (arXiv)

Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Sep 2018.

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Citing papers per year: 2021: 18; 2022: 24; 2023: 29; 2024: 16; 2025: 10; 2026: 2.

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  1. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  2. 2022Memory-Based Model Editing at Scalecited by 443
  3. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  4. 2021QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarizationcited by 233
  5. 2021Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Littlecited by 176
  6. 2021Explaining Answers with Entailment Treescited by 148
  7. 2023Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learningcited by 115
  8. 2022CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailmentcited by 113
  9. 2023Task Contamination: Language Models May Not Be Few-Shot Anymorecited by 98
  10. 2022ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Promptscited by 85
  11. 2021Multi-Modal Answer Validation for Knowledge-Based VQAcited by 78
  12. 2021DocNLI: A Large-scale Dataset for Document-level Natural Language Inferencecited by 75
38 more
  1. 2022Generating Scientific Claims for Zero-Shot Scientific Fact Checkingcited by 59
  2. 2021Can NLI Models Verify QA Systems' Predictions?cited by 46
  3. 2021Zero-shot Fact Verification by Claim Generationcited by 42
  4. 2022Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inferencecited by 40
  5. 2022Entailer: Answering Questions with Faithful and Truthful Chains of Reasoningcited by 38
  6. 2022Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvementcited by 37
  7. 2024Advancing Parameter Efficiency in Fine-tuning via Representation Editingcited by 37
  8. 2021FaVIQ: FAct Verification from Information-seeking Questionscited by 36
  9. 2022Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarizationcited by 34
  10. 2023Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Datacited by 33
  11. 2023Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Taskcited by 32
  12. 2023NevIR: Negation in Neural Information Retrievalcited by 28
  13. 2022Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methodscited by 27
  14. 2022Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarizationcited by 26
  15. 2023Zero-shot Faithful Factual Error Correctioncited by 26
  16. 2022Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learningcited by 25
  17. 2024HyperMoE: Towards Better Mixture of Experts via Transferring Among Expertscited by 25
  18. 2022REV: Information-Theoretic Evaluation of Free-Text Rationalescited by 22
  19. 2024MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimizationcited by 20
  20. 2021Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answeringcited by 17
  21. 2021Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regenerationcited by 17
  22. 2022OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Modelscited by 17
  23. 2022NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoningcited by 16
  24. 2025ARGUS: Hallucination and Omission Evaluation in Video-LLMscited by 16
  25. 2021Exploiting Reasoning Chains for Multi-hop Science Question Answeringcited by 15
  26. 2022Shortcomings of Question Answering Based Factuality Frameworks for Error Localizationcited by 15
  27. 2023Language Models with Rationalitycited by 15
  28. 2023Selective Generation for Controllable Language Modelscited by 15
  29. 2023GLoRE: Evaluating Logical Reasoning of Large Language Modelscited by 13
  30. 2024Enhancing Systematic Decompositional Natural Language Inference Using Informal Logiccited by 12
  31. 2024Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Taskscited by 12
  32. 2024Accurate and Nuanced Open-QA Evaluation Through Textual Entailmentcited by 12
  33. 2023TaskWeb: Selecting Better Source Tasks for Multi-task NLPcited by 11
  34. 2025Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaningcited by 10
  35. 2021Semantically Distributed Robust Optimization for Vision-and-Language Inferencecited by 9
  36. 2022Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuningcited by 9
  37. 2022Ask to Know More: Generating Counterfactual Explanations for Fake Claimscited by 8
  38. 2023Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphscited by 8

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