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# 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](https://md.mova.elamur.ai/free/en/docs/papers/pavlick-2019-inherent-disagreements-human-textual-inferences/)

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1. [SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation](https://aclanthology.org/2023.semeval-1.12/) *— 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)*
2. [SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)](https://aclanthology.org/2023.semeval-1.314/) *— 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)*
3. [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://aclanthology.org/2023.findings-emnlp.444/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
4. [Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging](https://aclanthology.org/2023.nodalida-1.37/) *— 2023 · Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)*
5. [Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation](https://arxiv.org/abs/2304.12443) *— 2023 · arXiv:2304.12443*
6. [Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance](https://aclanthology.org/2023.findings-emnlp.96/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
7. [VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights](https://aclanthology.org/2023.emnlp-main.718/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
8. [Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive](https://aclanthology.org/2023.emnlp-main.713/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
9. [We're Afraid Language Models Aren't Modeling Ambiguity](https://aclanthology.org/2023.emnlp-main.51/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
10. [What is the Real Intention behind this Question? Dataset Collection and Intention Classification](https://aclanthology.org/2023.acl-long.761/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [What's the Meaning of Superhuman Performance in Today's NLU?](https://aclanthology.org/2023.acl-long.697/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset](https://aclanthology.org/2023.law-1.25/) *— 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
13. [When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)](https://aclanthology.org/2023.starsem-1.4/) *— 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)*
14. [When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks](https://aclanthology.org/2023.emnlp-main.415/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
15. [Which Examples Should be Multiply Annotated? Active Learning When Annotators May Disagree](https://aclanthology.org/2023.findings-acl.658/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
16. [Whose Opinions Do Language Models Reflect?](https://arxiv.org/abs/2303.17548) *— 2023 · arXiv:2303.17548*
17. [Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA](https://aclanthology.org/2023.acl-long.569/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [You Are What You Annotate: Towards Better Models through Annotator Representations](https://aclanthology.org/2023.findings-emnlp.832/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
19. [Zero and Few-shot Semantic Parsing with Ambiguous Inputs](https://arxiv.org/abs/2306.00824) *— 2023 · arXiv:2306.00824*
20. [ALLSH: Active Learning Guided by Local Sensitivity and Hardness](https://aclanthology.org/2022.findings-naacl.99/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
21. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
22. [Analyzing Dynamic Adversarial Training Data in the Limit](https://aclanthology.org/2022.findings-acl.18/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
23. [Analyzing the Effects of Annotator Gender across NLP Tasks](https://aclanthology.org/2022.nlperspectives-1.2/) *— 2022 · Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022*
24. [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://aclanthology.org/2022.naacl-main.431/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
25. [ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements](https://aclanthology.org/2022.lrec-1.244/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
26. [Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference](https://aclanthology.org/2022.findings-acl.120/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
27. [Challenges and Strategies in Cross-Cultural NLP](https://aclanthology.org/2022.acl-long.482/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Clean or Annotate: How to Spend a Limited Data Collection Budget](https://aclanthology.org/2022.deeplo-1.17/) *— 2022 · Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing*
29. [CoRAL: a Context-aware Croatian Abusive Language Dataset](https://aclanthology.org/2022.findings-aacl.21/) *— 2022 · Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022*
30. [ComFact: A Benchmark for Linking Contextual Commonsense Knowledge](https://aclanthology.org/2022.findings-emnlp.120/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
31. [Compositional Evaluation on Japanese Textual Entailment and Similarity](https://aclanthology.org/2022.tacl-1.73/) *— 2022 · Transactions of the Association for Computational Linguistics*
32. [Disentangling Uncertainty in Machine Translation Evaluation](https://aclanthology.org/2022.emnlp-main.591/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
33. [Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://aclanthology.org/2022.findings-acl.79/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
34. [Investigating Reasons for Disagreement in Natural Language Inference](https://aclanthology.org/2022.tacl-1.78/) *— 2022 · Transactions of the Association for Computational Linguistics*
35. [Natural Language Deduction through Search over Statement Compositions](https://aclanthology.org/2022.findings-emnlp.358/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
36. [Norm Participation Grounds Language](https://aclanthology.org/2022.clasp-1.7/) *— 2022 · Proceedings of the 2022 CLASP Conference on (Dis)embodiment*
37. [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://aclanthology.org/2022.coling-1.319/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
38. [Stop Measuring Calibration When Humans Disagree](https://aclanthology.org/2022.emnlp-main.124/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
39. [Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment](https://aclanthology.org/2022.coling-1.359/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
40. [The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail](https://aclanthology.org/2022.acl-long.516/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [The ``Problem'''' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://aclanthology.org/2022.emnlp-main.731/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
42. [Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks](https://aclanthology.org/2022.naacl-main.13/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
43. [Uncovering Surprising Event Boundaries in Narratives](https://aclanthology.org/2022.wnu-1.1/) *— 2022 · Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)*
44. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
45. [(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys](https://aclanthology.org/2021.emnlp-main.27/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
46. [Agree to Disagree: Analysis of Inter-Annotator Disagreements in Human Evaluation of Machine Translation Output](https://aclanthology.org/2021.conll-1.18/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
47. [Analyzing Stereotypes in Generative Text Inference Tasks](https://aclanthology.org/2021.findings-acl.355/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
48. [Annotation Difficulties in Natural Language Inference](https://aclanthology.org/2021.stil-1.27/) *— 2021 · Proceedings of the 13th Brazilian Symposium in Information and Human Language Technology*
49. [Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning](https://aclanthology.org/2021.naacl-main.204/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
50. [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://arxiv.org/abs/2109.00725) *— 2021 · arXiv:2109.00725*
51. [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://aclanthology.org/2021.findings-acl.371/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
52. [Decontextualization: Making Sentences Stand-Alone](https://aclanthology.org/2021.tacl-1.27/) *— 2021 · Transactions of the Association for Computational Linguistics*
53. [Did they answer? Subjective acts and intents in conversational discourse](https://aclanthology.org/2021.naacl-main.129/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
54. [Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards](https://aclanthology.org/2021.iwcs-1.8/) *— 2021 · Proceedings of the 14th International Conference on Computational Semantics (IWCS)*
55. [DynaSent: A Dynamic Benchmark for Sentiment Analysis](https://aclanthology.org/2021.acl-long.186/) *— 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)*
56. [Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics](https://aclanthology.org/2021.nlp4posimpact-1.14/) *— 2021 · Proceedings of the 1st Workshop on NLP for Positive Impact*
57. [Identifying inherent disagreement in natural language inference](https://aclanthology.org/2021.naacl-main.390/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
58. [Interrater Disagreement Resolution: A Systematic Procedure to Reach Consensus in Annotation Tasks](https://aclanthology.org/2021.humeval-1.15/) *— 2021 · Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)*
59. [Learning with Different Amounts of Annotation: From Zero to Many Labels](https://aclanthology.org/2021.emnlp-main.601/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
60. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
61. [SemEval-2021 Task 12: Learning with Disagreements](https://aclanthology.org/2021.semeval-1.41/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
62. [Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality](https://aclanthology.org/2021.naacl-main.345/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
63. [Targeting the Benchmark: On Methodology in Current Natural Language Processing Research](https://aclanthology.org/2021.acl-short.85/) *— 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)*
64. [We Need to Consider Disagreement in Evaluation](https://aclanthology.org/2021.bppf-1.3/) *— 2021 · Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future*
65. [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://aclanthology.org/2021.acl-long.98/) *— 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)*
66. [``I''ll be there for you'''': The One with Understanding Indirect Answers](https://md.mova.elamur.ai/free/en/docs/papers/damgaard-2021-i-ll-be-there-you/) *— our card · 2021 · Proceedings of the 2nd Workshop on Computational Approaches to Discourse*
67. [(Re)construing Meaning in NLP](https://aclanthology.org/2020.acl-main.462/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
68. [Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models](https://arxiv.org/abs/2005.14709) *— 2020 · arXiv:2005.14709*
69. [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://aclanthology.org/2020.emnlp-main.154/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
70. [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://arxiv.org/abs/2012.05345) *— 2020 · arXiv:2012.05345 · Patterns, Volume 2, Issue 11, 100336. 2021*
71. [Evaluating Factuality in Generation with Dependency-level Entailment](https://aclanthology.org/2020.findings-emnlp.322/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
72. [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://aclanthology.org/2020.findings-emnlp.309/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
73. [Language (Technology) is Power: A Critical Survey of ``Bias'''' in NLP](https://aclanthology.org/2020.acl-main.485/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
74. [Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?](https://aclanthology.org/2020.findings-emnlp.390/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
75. [Natural Language Inference with Mixed Effects](https://aclanthology.org/2020.starsem-1.9/) *— 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics*
76. [OCNLI: Original Chinese Natural Language Inference](https://arxiv.org/abs/2010.05444) *— 2020 · arXiv:2010.05444*
77. [Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias](https://aclanthology.org/2020.law-1.6/) *— 2020 · Proceedings of the 14th Linguistic Annotation Workshop*
78. [Returning the N to NLP: Towards Contextually Personalized Classification Models](https://aclanthology.org/2020.acl-main.700/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
79. [Social Media Attributions in the Context of Water Crisis](https://aclanthology.org/2020.emnlp-main.109/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
80. [Thinking Like a Skeptic: Defeasible Inference in Natural Language](https://aclanthology.org/2020.findings-emnlp.418/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
81. [Uncertain Natural Language Inference](https://aclanthology.org/2020.acl-main.774/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
82. [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://aclanthology.org/2020.emnlp-main.734/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
83. [What Question Answering can Learn from Trivia Nerds](https://aclanthology.org/2020.acl-main.662/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
84. [Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement](https://aclanthology.org/2020.coling-main.422/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
85. [Probing Natural Language Inference Models through Semantic Fragments](https://arxiv.org/abs/1909.07521) *— 2019 · arXiv:1909.07521*
86. alfter-2025-grasp-comedi-shared-task-multi
87. alkhalifa-2026-optimizing-what-we-trust-reliability
88. andresen-2020-modeling-ambiguity-many-annotators-self
89. avais-2024-getting-logic-llms-annotating-natural
90. chen-2023-sources-noise-dialogue-how-deal
91. chen-2025-feels-feminine-me-understanding-perceived
92. elazar-2020-extraordinary-failure-complement-coercion-crowdsourcing
93. gajewska-2026-how-ethos-pathos-appeals-resonate
94. hemm-2024-are-you-serious-handling-disagreement
95. honda-2025-exploring-explanations-improves-robustness-context
96. hossain-2026-when-ground-truth-disagrees-human
97. hosseini-2024-scalable-domain-general-abstractive-proposition
98. huang-2026-deepfact-co-evolving-benchmarks-agents
99. huang-2026-forex-formal-verification-framework-explainable
100. ilinykh-2025-surprisal-reveals-diversity-gaps-image

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