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

1. Beyond Annotator Disagreement: Guideline-Induced Errors in Arabic Hate Speech Annotation — 2026 · Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)
2. Dataset Cartography for Implicit Discourse Relation Recognition: Promises and Pitfalls — 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 — 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference
4. EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
5. Explainable Semantic Textual Similarity via Dissimilar Span Detection — 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference
6. Extending Retag to Conversion Error Detection: A Case Study on SynTagRus Morphology — 2026 · Proceedings of the Ninth Workshop on Universal Dependencies (UDW 2026)
7. Gold Label Errors in the SciFact Benchmark: An LLM-Assisted Annotation Audit — 2026 · BioNLP 2026
8. Human Label Variation in Implicit Discourse Relation Recognition — 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference
9. UD-CHILDES-BG: a dependency treebank of Bulgarian child and child-directed speech — 2026 · Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)
10. Using Model Disagreement to Identify Unstable Regions in MT Evaluation — 2026 · Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
11. Annotating the Annotators: Analysis, Insights and Modelling from an Annotation Campaign on Persuasion Techniques Detection — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
12. Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
13. Analyzing Dataset Annotation Quality Management in the Wild — 2024 · Computational Linguistics
14. Interpreting Predictive Probabilities: Model Confidence or Human Label Variation? — 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
15. Large Language Models: A New Approach for Privacy Policy Analysis at Scale — 2024 · arXiv:2405.20900 · Computing, vol. 106, no. 12, pp. 3879-3903, 2024
16. Multi-Label Classification for Implicit Discourse Relation Recognition — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
17. ToMBench: Benchmarking Theory of Mind in Large Language Models — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
18. VariErr NLI: Separating Annotation Error from Human Label Variation — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
19. ActiveAED: A Human in the Loop Improves Annotation Error Detection — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
20. Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
21. Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling — 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)
22. 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)
23. Stop Measuring Calibration When Humans Disagree — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
24. 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
25. basile-2023-zero-shot-data-maps-efficient
26. calamai-2025-benchmarking-benchmarks-reproducing-climate-related
27. hossain-2026-when-ground-truth-disagrees-human
28. klie-2024-efficient-statistical-quality-estimation-data
29. merdjanovska-2024-noisebench-benchmarking-impact-real-label
30. merdjanovska-2025-token-level-metrics-detecting-incorrect
31. vladika-2025-facts-fade-fast-evaluating-memorization
32. weber-2024-donkii-characterizing-detecting-errors-instruction

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