mlsanityData to 7 Oct 2026

Identifying Well-formed Natural Language Questions

arXiv 1808.09419 ↗First posted 28 Aug 2018cs.CLcited by 29

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Abstract (arXiv)

Understanding search queries is a hard problem as it involves dealing with "word salad" text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Aug 2018.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 7; 2024: 3; 2025: 3; 2026: 1.

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  1. 2021MetaICL: Learning to Learn In Contextcited by 450
  2. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  3. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  4. 2023Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinementcited by 43
  5. 2023Pre-Training to Learn in Contextcited by 41
  6. 2021Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learningcited by 35
  7. 2021Exploring Universal Intrinsic Task Subspace via Prompt Tuningcited by 25
  8. 2021Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answeringcited by 19
  9. 2023Building Efficient Universal Classifiers with Natural Language Inferencecited by 19
  10. 2023Recyclable Tuning for Continual Pre-trainingcited by 17
  11. 2022Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Expertscited by 12
  12. 2022Interpretable AMR-Based Question Decomposition for Multi-hop Question Answeringcited by 12
17 more
  1. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
  2. 2024An Automatic Question Usability Evaluation Toolkitcited by 9
  3. 2022Few-shot Adaptation Works with UnpredicTable Datacited by 6
  4. 2024Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routingcited by 6
  5. 2023Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learningcited by 5
  6. 2024Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedbackcited by 5
  7. 2022Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systemscited by 4
  8. 2023Automating question generation from educational textcited by 3
  9. 2025You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Modelscited by 3
  10. 2021Keyword Extraction for Improved Document Retrieval in Conversational Searchcited by 1
  11. 2022KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLPcited by 1
  12. 2022Improving Question Answering with Generation of NQ-like Questionscited by 1
  13. 2022Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuningcited by 1
  14. 2025Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Modelscited by 1
  15. 2026RAG-Reflect: Agentic Retrieval-Augmented Generation with Reflections for Comment-Driven Code Maintenance on Stack Overflowcited by 1
  16. 2022A Syntax Aware BERT for Identifying Well-Formed Queries in a Curriculum Frameworkcited by 0
  17. 2025CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinementcited by 0