---
title: "Who cites Experts, Errors, and Context: A Large-Scale…"
description: "The papers of the Elamur Research library that cite «Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation», found…"
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---
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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.research.elamur.ai/free/en/papers/freitag-2021-experts-errors-context-large-scale/)

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

1. [Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation](https://aclanthology.org/2023.emnlp-demo.30/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
2. [Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration](https://aclanthology.org/2023.emnlp-main.798/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
3. [Toward Human-Like Evaluation for Natural Language Generation with Error Analysis](https://aclanthology.org/2023.acl-long.324/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Towards Explainable Evaluation Metrics for Machine Translation](https://arxiv.org/abs/2306.13041) *— 2023 · arXiv:2306.13041 · Journal of Machine Learning Research (JMLR), 2024, vol. 25, no. 75, p. 1-49*
5. [Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors](https://arxiv.org/abs/2302.08975) *— 2023 · arXiv:2302.08975*
6. [Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Language](https://aclanthology.org/2023.icon-1.28/) *— 2023 · Proceedings of the 20th International Conference on Natural Language Processing (ICON)*
7. [Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents](https://aclanthology.org/2023.emnlp-main.835/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
8. [Toxicity in Multilingual Machine Translation at Scale](https://aclanthology.org/2023.findings-emnlp.642/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
9. [Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level](https://aclanthology.org/2023.wmt-1.96/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
10. [UScore: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation](https://aclanthology.org/2023.eacl-main.27/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
11. [Visual Prediction Improves Zero-Shot Cross-Modal Machine Translation](https://aclanthology.org/2023.wmt-1.47/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
12. [eBLEU: Unexpectedly Good Machine Translation Evaluation Using Simple Word Embeddings](https://aclanthology.org/2023.wmt-1.61/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
13. [A Comparison of Data Filtering Methods for Neural Machine Translation](https://aclanthology.org/2022.amta-upg.22/) *— 2022 · Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)*
14. [A Natural Diet: Towards Improving Naturalness of Machine Translation Output](https://aclanthology.org/2022.findings-acl.263/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
15. [A new approach to calculating BERTScore for automatic assessment of translation quality](https://arxiv.org/abs/2203.05598) *— 2022 · arXiv:2203.05598*
16. [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.research.elamur.ai/free/en/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
17. [Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task](https://arxiv.org/abs/2210.09683) *— 2022 · arXiv:2210.09683*
18. [Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.55/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
19. [Alibaba-Translate China's Submission for WMT2022 Metrics Shared Task](https://aclanthology.org/2022.wmt-1.53/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
20. [An Overview on Machine Translation Evaluation](https://arxiv.org/abs/2202.11027) *— 2022 · arXiv:2202.11027*
21. [As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning](https://aclanthology.org/2022.acl-short.53/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
22. [Automatic Correction of Human Translations](https://aclanthology.org/2022.naacl-main.36/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
23. [BJTU-Toshiba's Submission to WMT22 Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.58/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
24. [Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand](https://aclanthology.org/2022.naacl-main.259/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
25. [COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task](https://aclanthology.org/2022.wmt-1.52/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
26. [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.60/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
27. [Consistent Human Evaluation of Machine Translation across Language Pairs](https://aclanthology.org/2022.amta-research.24/) *— 2022 · Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*
28. [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*
29. [DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages](https://aclanthology.org/2022.emnlp-main.532/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
30. [Does Summary Evaluation Survive Translation to Other Languages?](https://aclanthology.org/2022.naacl-main.173/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
31. [Efficient Machine Translation Corpus Generation](https://aclanthology.org/2022.amta-coco4mt.2/) *— 2022 · Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for Machine Translation)*
32. [Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric](https://aclanthology.org/2022.wmt-1.6/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
33. [Error Analysis of ToTTo Table-to-Text Neural NLG Models](https://aclanthology.org/2022.gem-1.43/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
34. [Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://aclanthology.org/2022.tacl-1.62/) *— 2022 · Transactions of the Association for Computational Linguistics*
35. [Evaluating Machine Translation in Cross-lingual E-Commerce Search](https://aclanthology.org/2022.amta-research.25/) *— 2022 · Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*
36. [Examining Large Pre-Trained Language Models for Machine Translation: What You Don”t Know about It](https://aclanthology.org/2022.wmt-1.84/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
37. [Findings of the 2022 Conference on Machine Translation (WMT22)](https://aclanthology.org/2022.wmt-1.1/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
38. [Findings of the First WMT Shared Task on Sign Language Translation (WMT-SLT22)](https://aclanthology.org/2022.wmt-1.71/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
39. [Findings of the IWSLT 2022 Evaluation Campaign](https://aclanthology.org/2022.iwslt-1.10/) *— 2022 · Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)*
40. [Findings of the WMT 2022 Shared Task on Chat Translation](https://aclanthology.org/2022.wmt-1.70/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
41. [Findings of the WMT 2022 Shared Task on Efficient Translation](https://aclanthology.org/2022.wmt-1.4/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
42. [From COMET to COMES – Can Summary Evaluation Benefit from Translation Evaluation?](https://aclanthology.org/2022.eval4nlp-1.3/) *— 2022 · Proceedings of the 3rd Workshop on Evaluation and Comparison of NLP Systems*
43. [GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation](https://aclanthology.org/2022.emnlp-main.787/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
44. [HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation](https://aclanthology.org/2022.lrec-1.2/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
45. [High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics](https://aclanthology.org/2022.tacl-1.47/) *— 2022 · Transactions of the Association for Computational Linguistics*
46. [HilMeMe: A Human-in-the-Loop Machine Translation Evaluation Metric Looking into Multi-Word Expressions](https://arxiv.org/abs/2211.05201) *— 2022 · arXiv:2211.05201*
47. [Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET](https://aclanthology.org/2022.aacl-main.83/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
48. [MS-COMET: More and Better Human Judgements Improve Metric Performance](https://aclanthology.org/2022.wmt-1.47/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
49. [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*
50. [Measuring Uncertainty in Translation Quality Evaluation (TQE)](https://aclanthology.org/2022.lrec-1.156/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
51. [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)*
52. [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)*
53. [Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets](https://aclanthology.org/2022.emnlp-main.135/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
54. [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*
55. [On Decoding Strategies for Neural Text Generators](https://aclanthology.org/2022.tacl-1.58/) *— 2022 · Transactions of the Association for Computational Linguistics*
56. [On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation](https://aclanthology.org/2022.naacl-main.161/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
57. [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)*
58. [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*
59. [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741) *— 2022 · arXiv:2212.09741*
60. [Quality Scoring of Source Words in Neural Translation Models](https://aclanthology.org/2022.emnlp-main.732/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
61. [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*
62. [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://arxiv.org/abs/2202.06935) *— 2022 · arXiv:2202.06935*
63. [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://md.research.elamur.ai/free/en/papers/freitag-2022-results-wmt22-metrics-shared-task/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
64. [Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement](https://arxiv.org/abs/2209.05695) *— 2022 · arXiv:2209.05695*
65. [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*
66. [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)*
67. [Survey of Low-Resource Machine Translation](https://aclanthology.org/2022.cl-3.6/) *— 2022 · Computational Linguistics*
68. [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*
69. [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*
70. [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)*
71. [Translation Error Detection as Rationale Extraction](https://aclanthology.org/2022.findings-acl.327/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
72. [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*
73. [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*
74. [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*
75. [Attainable Text-to-Text Machine Translation vs. Translation: Issues Beyond Linguistic Processing](https://aclanthology.org/2021.mtsummit-research.18/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
76. [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*
77. [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*
78. [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*
79. [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*
80. [MTEQA at WMT21 Metrics Shared Task](https://aclanthology.org/2021.wmt-1.110/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
81. [On nature and causes of observed MT errors](https://aclanthology.org/2021.mtsummit-research.14/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
82. [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*
83. [On the Stability of System Rankings at WMT](https://aclanthology.org/2021.wmt-1.56/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
84. [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)*
85. [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*
86. [Regressive Ensemble for Machine Translation Quality Evaluation](https://aclanthology.org/2021.wmt-1.112/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
87. [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*
88. [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*
89. [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)*
90. [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*
91. [Uncertainty-Aware Machine Translation Evaluation](https://aclanthology.org/2021.findings-emnlp.330/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
92. [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*
93. [What happens if you treat ordinal ratings as interval data? Human evaluations in NLP are even more under-powered than you think](https://aclanthology.org/2021.emnlp-main.703/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
94. [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*
95. [Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date overview](https://arxiv.org/abs/1605.04515) *— 2016 · arXiv:1605.04515*

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