---
title: "Who cites Annotation Error Detection: Analyzing the Past…"
description: "The papers of the Elamur Research library that cite «Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future», found in…"
canonical: "https://research.elamur.ai/free/en/papers/cl-2023-klie-annotation-error-detection-analyzing-past/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Annotation Error Detection: Analyzing the Past…](https://md.research.elamur.ai/free/en/papers/cl-2023-klie-annotation-error-detection-analyzing-past/) / cited by

# Who cites «Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future»

32 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/cl-2023-klie-annotation-error-detection-analyzing-past/)

1. [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)*
2. [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)*
3. [Detecting Potentially Under-annotated Explicit Discourse Connectives in the Penn Discourse Treebank (PDTB-3) with LLMs](https://aclanthology.org/2026.lrec-1.158/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
4. [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*
5. [Explainable Semantic Textual Similarity via Dissimilar Span Detection](https://aclanthology.org/2026.lrec-1.172/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
6. [Extending Retag to Conversion Error Detection: A Case Study on SynTagRus Morphology](https://aclanthology.org/2026.udw-1.28/) *— 2026 · Proceedings of the Ninth Workshop on Universal Dependencies (UDW 2026)*
7. [Gold Label Errors in the SciFact Benchmark: An LLM-Assisted Annotation Audit](https://aclanthology.org/2026.bionlp-1.9/) *— 2026 · BioNLP 2026*
8. [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*
9. [UD-CHILDES-BG: a dependency treebank of Bulgarian child and child-directed speech](https://aclanthology.org/2026.law-main.9/) *— 2026 · Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)*
10. [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)*
11. [When Ground Truth Disagrees: A Human-in-the-Loop Audit of Annotation Errors in High-Stakes Crash Narratives](https://aclanthology.org/2026.law-main.18/) *— 2026 · Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)*
12. [Annotating the Annotators: Analysis, Insights and Modelling from an Annotation Campaign on Persuasion Techniques Detection](https://aclanthology.org/2025.findings-acl.922/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
13. [Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance](https://aclanthology.org/2025.emnlp-main.1360/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
14. [Benchmarking the Benchmarks: Reproducing Climate-Related NLP Tasks](https://aclanthology.org/2025.findings-acl.925/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
15. [Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models](https://aclanthology.org/2025.findings-emnlp.487/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
16. [Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition](https://aclanthology.org/2025.findings-emnlp.827/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
17. [Analyzing Dataset Annotation Quality Management in the Wild](https://aclanthology.org/2024.cl-3.1/) *— 2024 · Computational Linguistics*
18. [Donkii: Characterizing and Detecting Errors in Instruction-Tuning Datasets](https://aclanthology.org/2024.law-1.19/) *— 2024 · Proceedings of the 18th Linguistic Annotation Workshop (LAW-XVIII)*
19. [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)*
20. [Large Language Models: A New Approach for Privacy Policy Analysis at Scale](https://arxiv.org/abs/2405.20900) *— 2024 · arXiv:2405.20900 · Computing, vol. 106, no. 12, pp. 3879-3903, 2024*
21. [Multi-Label Classification for Implicit Discourse Relation Recognition](https://aclanthology.org/2024.findings-acl.500/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
22. [NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition](https://aclanthology.org/2024.emnlp-main.1011/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
23. [On Efficient and Statistical Quality Estimation for Data Annotation](https://aclanthology.org/2024.acl-long.837/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [ToMBench: Benchmarking Theory of Mind in Large Language Models](https://aclanthology.org/2024.acl-long.847/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [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)*
26. [ActiveAED: A Human in the Loop Improves Annotation Error Detection](https://aclanthology.org/2023.findings-acl.562/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
27. [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://aclanthology.org/2023.acl-long.34/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling](https://aclanthology.org/2023.law-1.10/) *— 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
29. [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)*
30. [Zero-Shot Data Maps. Efficient Dataset Cartography Without Model Training](https://aclanthology.org/2023.findings-emnlp.554/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
31. [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*
32. [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*