mlsanityData to 7 Oct 2026

Confidence Modeling for Neural Semantic Parsing

arXiv 1805.04604 ↗First posted 11 May 2018cs.CLcited by 33

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Abstract (arXiv)

In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions are likely to be correct. Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying on attention scores.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2018.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 7; 2024: 6; 2025: 4; 2026: 2.

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  1. 2022Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectlycited by 86
  2. 2023Selectively Answering Ambiguous Questionscited by 63
  3. 2021Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answeringcited by 62
  4. 2021Knowing More About Questions Can Help: Improving Calibration in Question Answeringcited by 47
  5. 2022Calibrated Interpretation: Confidence Estimation in Semantic Parsingcited by 33
  6. 2023Learning to Reject with a Fixed Predictor: Application to Decontextualizationcited by 32
  7. 2021A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applicationscited by 24
  8. 2021Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Trainingcited by 19
  9. 2023Uncertainty-aware Language Modeling for Selective Question Answeringcited by 16
  10. 2024Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?cited by 15
  11. 2022Calibration of Machine Reading Systems at Scalecited by 11
  12. 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intentscited by 10
21 more
  1. 2025Confidence Estimation for Error Detection in Text-to-SQL Systemscited by 10
  2. 2021Toward Code Generation: A Survey and Lessons from Semantic Parsingcited by 8
  3. 2025Uncertainty Quantification in Retrieval Augmented Question Answeringcited by 8
  4. 2025Learning to Insert [PAUSE] Tokens for Better Reasoningcited by 8
  5. 2023Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Datacited by 7
  6. 2022Training Naturalized Semantic Parsers with Very Little Datacited by 6
  7. 2022Eeny, meeny, miny, moe. How to choose data for morphological inflectioncited by 6
  8. 2022Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQLcited by 6
  9. 2021The Case for Claim Difficulty Assessment in Automatic Fact Checkingcited by 5
  10. 2021Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLcited by 5
  11. 2025Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequenciescited by 5
  12. 2022Confidence-Guided Learning Process for Continuous Classification of Time Seriescited by 3
  13. 2026Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoningcited by 3
  14. 2023A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scorescited by 2
  15. 2026Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answeringcited by 2
  16. 2023Predicting generalization performance with correctness discriminatorscited by 1
  17. 2024Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanismcited by 1
  18. 2024Equipping Transformer with Random-Access Reading for Long-Context Understandingcited by 1
  19. 2024Handling Ontology Gaps in Semantic Parsingcited by 1
  20. 2024Defining and Evaluating Decision and Composite Risk in Language Models Applied to Natural Language Inferencecited by 1
  21. 2024Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsingcited by 1