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# Who cites «Inherent Disagreements in Human Textual Inferences»

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/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/pavlick-2019-inherent-disagreements-human-textual-inferences-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/pavlick-2019-inherent-disagreements-human-textual-inferences-3/)

1. [Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations](https://aclanthology.org/2026.findings-acl.1342/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning](https://aclanthology.org/2026.acl-srw.11/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
3. [Assessing the Difficulty of Inference Types in Natural Language Inference for Clinical Trials](https://aclanthology.org/2026.lrec-1.413/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
4. [Beyond Annotator Disagreement: Guideline-Induced Errors in Arabic Hate Speech Annotation](https://aclanthology.org/2026.law-main.5/) *— 2026 · Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)*
5. [Beyond Black-Box Labels: Interpretable Criteria for Diagnosing Subjective NLP Tasks](https://aclanthology.org/2026.findings-acl.1281/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models](https://arxiv.org/abs/2603.09993) *— 2026 · arXiv:2603.09993*
7. [Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation](https://aclanthology.org/2026.acl-industry.14/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)*
8. [Can LLMs Evaluate What They Cannot Annotate? Revisiting LLM Reliability in Hate Speech Detection](https://aclanthology.org/2026.lrec-1.341/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
9. [DIDECO: An Annotated Dataset for Intent Detection in Digital Communications](https://aclanthology.org/2026.lrec-1.542/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
10. [Dataset Cartography for Implicit Discourse Relation Recognition: Promises and Pitfalls](https://aclanthology.org/2026.codi-1.8/) *— 2026 · Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference (CODI-CRAC 2026)*
11. [Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective](https://aclanthology.org/2026.findings-acl.1103/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
12. [EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI](https://aclanthology.org/2026.findings-acl.65/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
13. [Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales](https://aclanthology.org/2026.nlperspectives-1.7/) *— 2026 · Proceedings of the the fifth edition of NLPerspectives*
14. [Gold Label Errors in the SciFact Benchmark: An LLM-Assisted Annotation Audit](https://aclanthology.org/2026.bionlp-1.9/) *— 2026 · BioNLP 2026*
15. [How Grounded is Wikipedia? A Study on Structured Evidential Support and Retrieval](https://aclanthology.org/2026.findings-acl.19/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
16. [How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI](https://arxiv.org/abs/2607.15870) *— 2026 · arXiv:2607.15870*
17. [Human Label Variation in Implicit Discourse Relation Recognition](https://aclanthology.org/2026.lrec-1.388/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
18. [In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions](https://aclanthology.org/2026.tacl-1.74/) *— 2026 · Transactions of the Association for Computational Linguistics*
19. [Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference](https://aclanthology.org/2026.acl-long.752/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [Position: From Noise to Signal to Selbstzweck - Reframing Human Label Variation in the Era of Post-training in NLP](https://aclanthology.org/2026.findings-acl.1190/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
21. [Selection Shapes the Boundary: A Preregistered Replication of Monotonicity and Label Agreement in Unselected NLI Populations](https://arxiv.org/abs/2607.19231) *— 2026 · arXiv:2607.19231*
22. [SynthLLM: An LLM-based Scalable Synthetic Data Generation Pipeline for Low-Resource Languages](https://aclanthology.org/2026.lrec-1.844/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
23. [Training and Evaluating with Human Label Variation: An Empirical Study](https://aclanthology.org/2026.cl-1.3/) *— 2026 · Computational Linguistics*
24. [Using Model Disagreement to Identify Unstable Regions in MT Evaluation](https://aclanthology.org/2026.eamt-1.22/) *— 2026 · Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)*
25. [A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition](https://aclanthology.org/2025.sigdial-1.18/) *— 2025 · Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
26. [A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI](https://aclanthology.org/2025.findings-acl.562/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
27. [AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context](https://aclanthology.org/2025.acl-long.1022/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic Discourse](https://aclanthology.org/2025.acl-long.120/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [Enhancing Small Language Models for Cross-Lingual Generalized Zero-Shot Classification with Soft Prompt Tuning](https://arxiv.org/abs/2503.19469) *— 2025 · arXiv:2503.19469*
30. [Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement](https://aclanthology.org/2025.findings-acl.293/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
31. [LeWiDi-2025 at NLPerspectives: Third Edition of the Learning with Disagreements Shared Task](https://aclanthology.org/2025.nlperspectives-1.16/) *— 2025 · Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP*
32. [Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models](https://aclanthology.org/2025.naacl-long.466/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
33. [Mining the uncertainty patterns of humans and models in the annotation of moral foundations and human values](https://aclanthology.org/2025.acl-long.1116/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [On Crowdsourcing Task Design for Discourse Relation Annotation](https://aclanthology.org/2025.comedi-1.2/) *— 2025 · Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation*
35. [On Reference (In-)Determinacy in Natural Language Inference](https://aclanthology.org/2025.findings-naacl.450/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
36. [PromotionGo at LeWiDi-2025: Enhancing Multilingual Irony Detection with Data-Augmented Ensembles and L1 Loss](https://aclanthology.org/2025.nlperspectives-1.21/) *— 2025 · Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP*
37. [Revisiting Active Learning under (Human) Label Variation](https://aclanthology.org/2025.nlperspectives-1.7/) *— 2025 · Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP*
38. [Threading the Needle: Reweaving Chain-of-Thought Reasoning to Explain Human Label Variation](https://aclanthology.org/2025.emnlp-main.1682/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
39. [𝛿-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation](https://aclanthology.org/2025.acl-long.1517/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
40. [Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree](https://aclanthology.org/2024.emnlp-main.1221/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
41. [Advancing Annotation for Continuous Data in Swiss German Sign Language](https://aclanthology.org/2024.signlang-1.1/) *— 2024 · Proceedings of the LREC-COLING 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources*
42. [AmbiFC: Fact-Checking Ambiguous Claims with Evidence](https://aclanthology.org/2024.tacl-1.1/) *— 2024 · Transactions of the Association for Computational Linguistics*
43. [Conditional and Modal Reasoning in Large Language Models](https://aclanthology.org/2024.emnlp-main.222/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
44. [Conformal Prediction for Natural Language Processing: A Survey](https://aclanthology.org/2024.tacl-1.82/) *— 2024 · Transactions of the Association for Computational Linguistics*
45. [Corpus Considerations for Annotator Modeling and Scaling](https://aclanthology.org/2024.naacl-long.59/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
46. [Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation](https://aclanthology.org/2024.findings-emnlp.199/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
47. [D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation](https://aclanthology.org/2024.emnlp-main.1029/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
48. [Don''t Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations](https://aclanthology.org/2024.uncertainlp-1.11/) *— 2024 · Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)*
49. [Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://aclanthology.org/2024.emnlp-main.531/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
50. [Evaluating Large Language Models for Health-related Queries with Presuppositions](https://aclanthology.org/2024.findings-acl.850/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
51. [Exploring Continual Learning of Compositional Generalization in NLI](https://aclanthology.org/2024.tacl-1.51/) *— 2024 · Transactions of the Association for Computational Linguistics*
52. [Exploring Soft-Label Training for Implicit Discourse Relation Recognition](https://aclanthology.org/2024.codi-1.11/) *— 2024 · Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)*
53. [Federated Learning for Exploiting Annotators' Disagreements in Natural Language Processing](https://aclanthology.org/2024.tacl-1.35/) *— 2024 · Transactions of the Association for Computational Linguistics*
54. [First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models](https://aclanthology.org/2024.naacl-long.128/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
55. [Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to Luxembourgish](https://aclanthology.org/2024.sigul-1.13/) *— 2024 · Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024*
56. [Foundational Challenges in Assuring Alignment and Safety of Large Language Models](https://arxiv.org/abs/2404.09932) *— 2024 · arXiv:2404.09932*
57. [GermEval2024 Shared Task: GerMS-Detect – Sexism Detection in German Online News Fora](https://aclanthology.org/2024.germeval-2.1/) *— 2024 · Proceedings of GermEval 2024 Task 1 GerMS-Detect Workshop on Sexism Detection in German Online News Fora (GerMS-Detect 2024)*
58. [Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?](https://aclanthology.org/2024.eacl-short.24/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
59. [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://aclanthology.org/2024.acl-long.745/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [Learning to Refine with Fine-Grained Natural Language Feedback](https://aclanthology.org/2024.findings-emnlp.716/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
61. [Leveraging Annotator Disagreement for Text Classification](https://aclanthology.org/2024.icnlsp-1.1/) *— 2024 · Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024)*
62. [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://aclanthology.org/2024.emnlp-main.499/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
63. [Multi-Task Learning with Adapters for Plausibility Prediction: Bridging the Gap or Falling into the Trenches?](https://aclanthology.org/2024.insights-1.18/) *— 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP*
64. [SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives](https://md.mova.elamur.ai/free/en/docs/papers/nizamani-2024-siga-naturalistic-nli-dataset-english/) *— our card · 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
65. [The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation](https://arxiv.org/abs/2405.01299) *— 2024 · arXiv:2405.01299 · https://aclanthology.org/2024.nlperspectives-1.11/*
66. [The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://aclanthology.org/2024.naacl-long.126/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
67. [The Unreasonable Effectiveness of Easy Training Data for Hard Tasks](https://aclanthology.org/2024.acl-long.378/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
68. [Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification](https://aclanthology.org/2024.acl-long.13/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [VariErr NLI: Separating Annotation Error from Human Label Variation](https://aclanthology.org/2024.acl-long.123/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
70. [What''s Mine becomes Yours: Defining, Annotating and Detecting Context-Dependent Paraphrases in News Interview Dialogs](https://aclanthology.org/2024.emnlp-main.52/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
71. [``Seeing the Big through the Small'''': Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?](https://aclanthology.org/2024.findings-emnlp.842/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
72. [``Tell me who you are and I tell you how you argue'''': Predicting Stances and Arguments for Stakeholder Groups](https://aclanthology.org/2024.findings-naacl.128/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
73. [ALANNO: An Active Learning Annotation System for Mortals](https://aclanthology.org/2023.eacl-demo.26/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations*
74. [Analyzing Cognitive Plausibility of Subword Tokenization](https://aclanthology.org/2023.emnlp-main.272/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
75. [Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://md.mova.elamur.ai/free/en/docs/papers/cl-2023-klie-annotation-error-detection-analyzing-past/) *— our card · 2023 · Computational Linguistics*
76. [Are Language Models Worse than Humans at Following Prompts? It's Complicated](https://aclanthology.org/2023.findings-emnlp.514/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
77. [COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models](https://aclanthology.org/2023.eacl-main.213/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
78. [Can Large Language Models Capture Dissenting Human Voices?](https://aclanthology.org/2023.emnlp-main.278/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
79. [Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs](https://arxiv.org/abs/2311.09469) *— 2023 · arXiv:2311.09469*
80. [Collective Human Opinions in Semantic Textual Similarity](https://aclanthology.org/2023.tacl-1.56/) *— 2023 · Transactions of the Association for Computational Linguistics*
81. [Common Law Annotations: Investigating the Stability of Dialog System Output Annotations](https://aclanthology.org/2023.findings-acl.780/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
82. [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://aclanthology.org/2023.acl-long.486/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
83. [Culturally Aware Natural Language Inference](https://aclanthology.org/2023.findings-emnlp.509/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
84. [Curing the SICK and Other NLI Maladies](https://aclanthology.org/2023.cl-1.5/) *— 2023 · Computational Linguistics*
85. [DISCO: Distilling Counterfactuals with Large Language Models](https://aclanthology.org/2023.acl-long.302/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
86. [Data Factors for Better Compositional Generalization](https://aclanthology.org/2023.emnlp-main.898/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
87. [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://aclanthology.org/2023.tacl-1.57/) *— 2023*
88. [Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo](https://aclanthology.org/2023.findings-acl.287/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
89. [Ecologically Valid Explanations for Label Variation in NLI](https://aclanthology.org/2023.findings-emnlp.712/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
90. [Expectations over Unspoken Alternatives Predict Pragmatic Inferences](https://md.mova.elamur.ai/free/en/docs/papers/hu-2023-expectations-over-unspoken-alternatives-predict/) *— our card · 2023*
91. [Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback](https://aclanthology.org/2023.acl-long.344/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
92. [FairPrism: Evaluating Fairness-Related Harms in Text Generation](https://aclanthology.org/2023.acl-long.343/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
93. [From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification](https://aclanthology.org/2023.emnlp-main.594/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
94. [Hate Speech Classifiers Learn Normative Social Stereotypes](https://aclanthology.org/2023.tacl-1.18/) *— 2023 · Transactions of the Association for Computational Linguistics*
95. [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://aclanthology.org/2023.findings-acl.202/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
96. [LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset](https://aclanthology.org/2023.findings-acl.378/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
97. [No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference](https://aclanthology.org/2023.law-1.20/) *— 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
98. [PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments](https://aclanthology.org/2023.conll-1.9/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
99. [SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration](https://aclanthology.org/2023.acl-long.370/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
100. [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)*

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