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# Who cites «Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation» · page 4

395 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/freitag-2021-experts-errors-context-large-scale/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/freitag-2021-experts-errors-context-large-scale/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/freitag-2021-experts-errors-context-large-scale-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/freitag-2021-experts-errors-context-large-scale-3/) · **4**

1. [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://aclanthology.org/2022.emnlp-main.288/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
2. [Measuring Uncertainty in Translation Quality Evaluation (TQE)](https://aclanthology.org/2022.lrec-1.156/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
3. [Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents](https://aclanthology.org/2022.acl-long.287/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [NJUNLP's Participation for the WMT2022 Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.57/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
5. [Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis](https://aclanthology.org/2022.findings-emnlp.489/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
6. [On Decoding Strategies for Neural Text Generators](https://aclanthology.org/2022.tacl-1.58/) *— 2022 · Transactions of the Association for Computational Linguistics*
7. [On reporting scores and agreement for error annotation tasks](https://aclanthology.org/2022.gem-1.26/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
8. [On the Effectiveness of Automated Metrics for Text Generation Systems](https://aclanthology.org/2022.findings-emnlp.108/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
9. [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741) *— 2022 · arXiv:2212.09741*
10. [Quality-Aware Decoding for Neural Machine Translation](https://aclanthology.org/2022.naacl-main.100/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
11. [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://arxiv.org/abs/2202.06935) *— 2022 · arXiv:2202.06935*
12. [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://md.mova.elamur.ai/free/en/docs/papers/freitag-2022-results-wmt22-metrics-shared-task/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
13. [Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement](https://arxiv.org/abs/2209.05695) *— 2022 · arXiv:2209.05695*
14. [Searching for COMETINHO: The Little Metric That Could](https://aclanthology.org/2022.eamt-1.9/) *— 2022 · Proceedings of the 23rd Annual Conference of the European Association for Machine Translation*
15. [Searching for a Higher Power in the Human Evaluation of MT](https://aclanthology.org/2022.wmt-1.7/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
16. [Survey of Low-Resource Machine Translation](https://aclanthology.org/2022.cl-3.6/) *— 2022 · Computational Linguistics*
17. [TRUE: Re-evaluating Factual Consistency Evaluation](https://aclanthology.org/2022.naacl-main.287/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
18. [The Cross-lingual Conversation Summarization Challenge](https://aclanthology.org/2022.inlg-genchal.2/) *— 2022 · Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges*
19. [Toward More Effective Human Evaluation for Machine Translation](https://aclanthology.org/2022.humeval-1.7/) *— 2022 · Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)*
20. [Translation Error Detection as Rationale Extraction](https://aclanthology.org/2022.findings-acl.327/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
21. [Transparent Human Evaluation for Image Captioning](https://aclanthology.org/2022.naacl-main.254/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
22. [Twist Decoding: Diverse Generators Guide Each Other](https://aclanthology.org/2022.emnlp-main.326/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
23. [Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task](https://aclanthology.org/2021.wmt-1.111/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
24. [Findings of the 2021 Conference on Machine Translation (WMT21)](https://aclanthology.org/2021.wmt-1.1/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
25. [Just Ask! Evaluating Machine Translation by Asking and Answering Questions](https://aclanthology.org/2021.wmt-1.58/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
26. [Learning Compact Metrics for MT](https://aclanthology.org/2021.emnlp-main.58/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
27. [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*
28. [MTEQA at WMT21 Metrics Shared Task](https://aclanthology.org/2021.wmt-1.110/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
29. [On nature and causes of observed MT errors](https://aclanthology.org/2021.mtsummit-research.14/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
30. [On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings](https://aclanthology.org/2021.emnlp-main.596/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
31. [On the Stability of System Rankings at WMT](https://aclanthology.org/2021.wmt-1.56/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
32. [Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort](https://aclanthology.org/2021.acl-long.242/) *— 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)*
33. [Perturbation CheckLists for Evaluating NLG Evaluation Metrics](https://aclanthology.org/2021.emnlp-main.575/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
34. [Regressive Ensemble for Machine Translation Quality Evaluation](https://aclanthology.org/2021.wmt-1.112/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
35. [Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain](https://aclanthology.org/2021.wmt-1.73/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
36. [The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results](https://aclanthology.org/2021.eval4nlp-1.17/) *— 2021 · Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems*
37. [The statistical advantage of automatic NLG metrics at the system level](https://aclanthology.org/2021.acl-long.533/) *— 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)*
38. [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://aclanthology.org/2021.wmt-1.57/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
39. [Uncertainty-Aware Machine Translation Evaluation](https://aclanthology.org/2021.findings-emnlp.330/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
40. [Underreporting of errors in NLG output, and what to do about it](https://aclanthology.org/2021.inlg-1.14/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
41. [cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE](https://aclanthology.org/2021.wmt-1.109/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
42. [Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date overview](https://arxiv.org/abs/1605.04515) *— 2016 · arXiv:1605.04515*
43. buma-2025-two-step-automatic-post-editing
44. burg-2025-aligning-black-box-language-models
45. chan-2026-xtom-exploring-multilingual-theory-mind
46. chirkova-2026-llm-as-qualitative-judge-automating
47. court-2026-llms-real-world-evaluating-ai
48. demeter-2023-multi-domain-summarization-leaderboards-practice
49. deviyani-2025-contextual-metric-meta-evaluation-measuring
50. diianni-2025-don-t-sweat-small-stuff
51. donati-2025-do-large-language-models-understand
52. eigler-2026-llm-as-meta-judge-synthetic
53. fischer-2025-swissadt-audio-description-translation-system
54. fluckiger-2025-comparison-translation-performance-between-deepl
55. fujita-2021-attainable-text-text-machine-translation
56. grubisic-2025-irb-mt-wmt25-translation-task
57. hamalainen-2025-nlg-evaluation-modern-student-assessment
58. hirasawa-2023-visual-prediction-improves-zero-shot
59. hofman-2026-maps-multilingual-benchmark-agent-performance
60. howcroft-2021-what-happens-if-you-treat
61. huidrom-2025-ask-me-like-i-m
62. jain-2022-quality-scoring-source-words-neural
63. kasner-2026-llms-as-span-annotators-comparative
64. laban-2022-near-negative-distinction-giving-second
65. larionov-2023-team-nllg-submission-eval4nlp-2023
66. leiter-2024-bmx-boosting-natural-language-generation
67. leteno-2025-histoiresmorales-french-dataset-assessing-moral
68. li-2024-reprohum-0033-3-comparable-relative
69. li-2024-what-have-we-achieved-non
70. lin-2025-self-vocabularizing-training-neural-machine
71. liu-2024-beyond-human-only-evaluating-human
72. liu-2024-evaluation-briefs-drawing-translation-studies
73. liu-2024-hd-eval-aligning-large-language
74. liu-2024-towards-global-ai-inclusivity-large
75. liu-2025-global-ai-inclusivity-large-scale
76. maharaj-2024-evaluation-continual-improvement-enterprise-ai
77. marchisio-2022-systematic-style-differences-between-unsupervised
78. matsuda-2025-vela-llm-hybrid-as-judge
79. miltenburg-2024-reprohum-0033-03-how-reproducible
80. nakazawa-2025-findings-first-patent-claims-translation
81. nakhle-2023-l-evaluation-de-la-traduction
82. nedey-2025-la-traduction-automatique-dialectale-etat
83. obadic-2023-c-xnli-croatian-extension-xnli
84. ray-2026-tau-multilingual-benchmarking-voice-agents
85. simard-2024-position-paper-should-machine-translation
86. song-2025-structured-document-translation-format-reinforcement
87. sosa-2025-rubrik-s-cube-testing-new
88. stap-2024-fine-tuning-paradox-boosting-translation
89. su-2023-one-embedder-any-task-instruction
90. sundararajan-2022-error-analysis-totto-table-text
91. wang-2025-contrastscore-higher-quality-less-biased
92. xiao-2025-machine-translation-literacy-how-lay
93. yuksel-2022-efficient-machine-translation-corpus-generation
94. yuksel-2023-evolvemt-ensemble-mt-engine-improving
95. zhao-2025-can-llms-help-sun-wukong

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