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home / docs / papers / cited-by / Who cites «Inherent Disagreements in Human Textual Inferences» · page 2

Who cites «Inherent Disagreements in Human Textual Inferences» · page 2

240 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

Pages: 1 · 2 · 3

  1. SemEval-2023 Task 11: Learning with Disagreements (LeWiDi) — 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
  2. The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  3. Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging — 2023 · Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
  4. Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation — 2023 · arXiv:2304.12443
  5. Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  6. VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  7. Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  8. We're Afraid Language Models Aren't Modeling Ambiguity — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  9. What is the Real Intention behind this Question? Dataset Collection and Intention Classification — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  10. What's the Meaning of Superhuman Performance in Today's NLU? — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  11. When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset — 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)
  12. When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs) — 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)
  13. When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  14. Which Examples Should be Multiply Annotated? Active Learning When Annotators May Disagree — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  15. Whose Opinions Do Language Models Reflect? — 2023 · arXiv:2303.17548
  16. Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  17. You Are What You Annotate: Towards Better Models through Annotator Representations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  18. Zero and Few-shot Semantic Parsing with Ambiguous Inputs — 2023 · arXiv:2306.00824
  19. ALLSH: Active Learning Guided by Local Sensitivity and Hardness — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
  20. ANLIzing the Adversarial Natural Language Inference Dataset — 2022 · Proceedings of the Society for Computation in Linguistics 2022
  21. Analyzing Dynamic Adversarial Training Data in the Limit — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  22. Analyzing the Effects of Annotator Gender across NLP Tasks — 2022 · Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
  23. Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  24. ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  25. Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  26. Challenges and Strategies in Cross-Cultural NLP — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  27. Clean or Annotate: How to Spend a Limited Data Collection Budget — 2022 · Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
  28. CoRAL: a Context-aware Croatian Abusive Language Dataset — 2022 · Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
  29. ComFact: A Benchmark for Linking Contextual Commonsense Knowledge — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  30. Compositional Evaluation on Japanese Textual Entailment and Similarity — 2022 · Transactions of the Association for Computational Linguistics
  31. Disentangling Uncertainty in Machine Translation Evaluation — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  32. Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  33. Investigating Reasons for Disagreement in Natural Language Inference — 2022 · Transactions of the Association for Computational Linguistics
  34. Natural Language Deduction through Search over Statement Compositions — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  35. Norm Participation Grounds Language — 2022 · Proceedings of the 2022 CLASP Conference on (Dis)embodiment
  36. Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  37. Stop Measuring Calibration When Humans Disagree — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  38. Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  39. The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  40. The ``Problem'''' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  41. Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  42. Uncovering Surprising Event Boundaries in Narratives — 2022 · Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)
  43. WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation — 2022 · arXiv:2201.05955
  44. (Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  45. Agree to Disagree: Analysis of Inter-Annotator Disagreements in Human Evaluation of Machine Translation Output — 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning
  46. Analyzing Stereotypes in Generative Text Inference Tasks — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
  47. Annotation Difficulties in Natural Language Inference — 2021 · Proceedings of the 13th Brazilian Symposium in Information and Human Language Technology
  48. Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  49. Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond — 2021 · arXiv:2109.00725
  50. Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
  51. Decontextualization: Making Sentences Stand-Alone — 2021 · Transactions of the Association for Computational Linguistics
  52. Did they answer? Subjective acts and intents in conversational discourse — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  53. Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards — 2021 · Proceedings of the 14th International Conference on Computational Semantics (IWCS)
  54. DynaSent: A Dynamic Benchmark for Sentiment Analysis — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
  55. Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics — 2021 · Proceedings of the 1st Workshop on NLP for Positive Impact
  56. Identifying inherent disagreement in natural language inference — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  57. Interrater Disagreement Resolution: A Systematic Procedure to Reach Consensus in Annotation Tasks — 2021 · Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)
  58. Learning with Different Amounts of Annotation: From Zero to Many Labels — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  59. Multitask Prompted Training Enables Zero-Shot Task Generalization — 2021 · arXiv:2110.08207
  60. SemEval-2021 Task 12: Learning with Disagreements — 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
  61. Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  62. Targeting the Benchmark: On Methodology in Current Natural Language Processing Research — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
  63. We Need to Consider Disagreement in Evaluation — 2021 · Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future
  64. What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
  65. ``I''ll be there for you'''': The One with Understanding Indirect Answers — our card · 2021 · Proceedings of the 2nd Workshop on Computational Approaches to Discourse
  66. (Re)construing Meaning in NLP — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  67. Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models — 2020 · arXiv:2005.14709
  68. CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  69. Data and its (dis)contents: A survey of dataset development and use in machine learning research — 2020 · arXiv:2012.05345 · Patterns, Volume 2, Issue 11, 100336. 2021
  70. Evaluating Factuality in Generation with Dependency-level Entailment — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  71. HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  72. Language (Technology) is Power: A Critical Survey of ``Bias'''' in NLP — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  73. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  74. Natural Language Inference with Mixed Effects — 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
  75. OCNLI: Original Chinese Natural Language Inference — 2020 · arXiv:2010.05444
  76. Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias — 2020 · Proceedings of the 14th Linguistic Annotation Workshop
  77. Returning the N to NLP: Towards Contextually Personalized Classification Models — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  78. Social Media Attributions in the Context of Water Crisis — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  79. Thinking Like a Skeptic: Defeasible Inference in Natural Language — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  80. Uncertain Natural Language Inference — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  81. What Can We Learn from Collective Human Opinions on Natural Language Inference Data? — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  82. What Question Answering can Learn from Trivia Nerds — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  83. Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
  84. Probing Natural Language Inference Models through Semantic Fragments — 2019 · arXiv:1909.07521
  85. alfter-2025-grasp-comedi-shared-task-multi
  86. alkhalifa-2026-optimizing-what-we-trust-reliability
  87. andresen-2020-modeling-ambiguity-many-annotators-self
  88. avais-2024-getting-logic-llms-annotating-natural
  89. chen-2023-sources-noise-dialogue-how-deal
  90. chen-2025-feels-feminine-me-understanding-perceived
  91. elazar-2020-extraordinary-failure-complement-coercion-crowdsourcing
  92. gajewska-2026-how-ethos-pathos-appeals-resonate
  93. hemm-2024-are-you-serious-handling-disagreement
  94. honda-2025-exploring-explanations-improves-robustness-context
  95. hossain-2026-when-ground-truth-disagrees-human
  96. hosseini-2024-scalable-domain-general-abstractive-proposition
  97. huang-2026-deepfact-co-evolving-benchmarks-agents
  98. huang-2026-forex-formal-verification-framework-explainable
  99. ilinykh-2025-surprisal-reveals-diversity-gaps-image
  100. islam-2026-cognac-semeval-2026-task-5

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