# Identifying Well-formed Natural Language Questions

arXiv: https://arxiv.org/abs/1808.09419 · HTML: https://mlsanity.com/paper/1808.09419/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 29 of 29)

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- [Building Efficient Universal Classifiers with Natural Language Inference](https://mlsanity.com/paper/2312.17543/) (2023)
- [Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering](https://mlsanity.com/paper/2109.07009/) (2021)
- [Recyclable Tuning for Continual Pre-training](https://mlsanity.com/paper/2305.08702/) (2023)
- [Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts](https://mlsanity.com/paper/2205.12701/) (2022)
- [Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering](https://mlsanity.com/paper/2206.08486/) (2022)
- [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://mlsanity.com/paper/2302.08143/) (2023)
- [An Automatic Question Usability Evaluation Toolkit](https://mlsanity.com/paper/2405.20529/) (2024)
- [Few-shot Adaptation Works with UnpredicTable Data](https://mlsanity.com/paper/2208.01009/) (2022)
- [Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing](https://mlsanity.com/paper/2404.12535/) (2024)
- [Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning](https://mlsanity.com/paper/2312.17055/) (2023)
- [Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedback](https://mlsanity.com/paper/2405.17658/) (2024)
- [Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems](https://mlsanity.com/paper/2206.04187/) (2022)
- [Automating question generation from educational text](https://mlsanity.com/paper/2309.15004/) (2023)
- [You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2506.11103/) (2025)
- [Keyword Extraction for Improved Document Retrieval in Conversational Search](https://mlsanity.com/paper/2109.05979/) (2021)
- [KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP](https://mlsanity.com/paper/2206.10265/) (2022)
- [Improving Question Answering with Generation of NQ-like Questions](https://mlsanity.com/paper/2210.06599/) (2022)
- [Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning](https://mlsanity.com/paper/2210.13311/) (2022)
- [Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models](https://mlsanity.com/paper/2510.12032/) (2025)
- [RAG-Reflect: Agentic Retrieval-Augmented Generation with Reflections for Comment-Driven Code Maintenance on Stack Overflow](https://mlsanity.com/paper/2604.22217/) (2026)
- [A Syntax Aware BERT for Identifying Well-Formed Queries in a Curriculum Framework](https://mlsanity.com/paper/2208.09912/) (2022)
- [CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement](https://mlsanity.com/paper/2510.12029/) (2025)

## 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.