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# Who cites «Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics»

203 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/mathur-2020-tangled-up-bleu-reevaluating-evaluation/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mathur-2020-tangled-up-bleu-reevaluating-evaluation-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mathur-2020-tangled-up-bleu-reevaluating-evaluation-3/)

1. [A Critical Study of Automatic Evaluation in Sign Language Translation](https://aclanthology.org/2026.lrec-1.749/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [Automated evaluation of LLMs for effective machine translation of Mandarin Chinese to English](https://arxiv.org/abs/2603.09998) *— 2026 · arXiv:2603.09998*
3. [Benchmarking Speech-to-Speech Translation Models](https://arxiv.org/abs/2606.03241) *— 2026 · arXiv:2606.03241*
4. [Can We Triage LLM Translation Errors in Classical Texts Without Human References? Source Novelty, GEMBA Scoring, and Budgeted Review through Pali-to-English Translation](https://arxiv.org/abs/2609.14963) *— 2026 · arXiv:2609.14963*
5. [Contextual Diversity Measure (CDM) for Controllable Story Generation in Large Language Models](https://aclanthology.org/2026.acl-srw.98/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
6. [Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations](https://aclanthology.org/2026.eamt-1.43/) *— 2026 · Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)*
7. [Diagnose, Then Repair: A Two-Stage MQM-Guided Post-Editing Framework for Domain-Specific Machine Translation](https://aclanthology.org/2026.acl-industry.115/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)*
8. [From Outliers to Errors: Auditing Pali-to-English LLM Translations with Multi-Reference Adjudication](https://arxiv.org/abs/2606.01136) *— 2026 · arXiv:2606.01136*
9. [M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models](https://arxiv.org/abs/2608.03803) *— 2026 · arXiv:2608.03803*
10. [Mind Which Bird You Favour: Parameterizing Adequacy-Fluency Balance in Meta-Evaluation of Machine Translation](https://arxiv.org/abs/2609.14795) *— 2026 · arXiv:2609.14795*
11. [NLG Evaluation: Past, Present, Future](https://aclanthology.org/2026.retroeval-main.2/) *— 2026 · Proceedings of the 1st Symposium on Natural Language Generation Evaluations*
12. [Referenceless Evaluation of Machine Translation Models by Ranking Performance in Romanian to English Translate-train Settings](https://aclanthology.org/2026.lrec-1.693/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
13. [SemiAdapt: Semi-Supervised and Efficient LoRA-Based Domain Adaptation for Low-Resource Irish Machine Translation with Transformers](https://aclanthology.org/2026.lrec-1.801/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
14. [TQLite: Multi-LLM Jury Guided Distillation for Real-time MQM Translation Quality Evaluation](https://arxiv.org/abs/2608.02975) *— 2026 · arXiv:2608.02975*
15. [Unified Multi-Dialectal Neural Machine Translation for Bangla Using the Dwadash Benchmark Corpus](https://arxiv.org/abs/2608.12018) *— 2026 · arXiv:2608.12018*
16. [Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains](https://aclanthology.org/2026.findings-acl.1145/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
17. [``Be My Cheese?'''': Cultural Nuance Benchmarking for Machine Translation in Multilingual LLMs](https://aclanthology.org/2026.gem-main.6/) *— 2026 · Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)*
18. [Analyzing and Evaluating Correlation Measures in NLG Meta-Evaluation](https://aclanthology.org/2025.naacl-long.111/) *— 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)*
19. [BLEUBERI: BLEU is a surprisingly effective reward for instruction following](https://arxiv.org/abs/2505.11080) *— 2025 · arXiv:2505.11080*
20. [Better Aligned with Survey Respondents or Training Data? Unveiling Political Leanings of LLMs on U.S. Supreme Court Cases](https://aclanthology.org/2025.l2m2-1.16/) *— 2025 · Proceedings of the First Workshop on Large Language Model Memorization (L2M2)*
21. [Better Late Than Never: Meta-Evaluation of Latency Metrics for Simultaneous Speech-to-Text Translation](https://arxiv.org/abs/2509.17349) *— 2025 · arXiv:2509.17349*
22. [Context-Informed Machine Translation of Manga using Multimodal Large Language Models](https://aclanthology.org/2025.coling-main.232/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
23. [D\''ej\`a Vu: Multilingual LLM Evaluation through the Lens of Machine Translation Evaluation](https://arxiv.org/abs/2504.11829) *— 2025 · arXiv:2504.11829*
24. [Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering](https://arxiv.org/abs/2504.07583) *— 2025 · arXiv:2504.07583*
25. [Estimating Machine Translation Difficulty](https://aclanthology.org/2025.findings-emnlp.1317/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
26. [Evaluating the Feasibility of Using ChatGPT for Cross-cultural Survey Translation](https://aclanthology.org/2025.konvens-1.16/) *— 2025 · Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Long and Short Papers*
27. [Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks](https://aclanthology.org/2025.coling-main.688/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
28. [Findings of the AmericasNLP 2025 Shared Tasks on Machine Translation, Creation of Educational Material, and Translation Metrics for Indigenous Languages of the Americas](https://aclanthology.org/2025.americasnlp-1.16/) *— 2025 · Proceedings of the Fifth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)*
29. [Findings of the WAT 2025 Shared Task on Japanese-English Article-level News Translation](https://aclanthology.org/2025.wat-1.8/) *— 2025 · Proceedings of the Twelfth Workshop on Asian Translation (WAT 2025)*
30. [Findings of the WMT25 General Machine Translation Shared Task: Time to Stop Evaluating on Easy Test Sets](https://aclanthology.org/2025.wmt-1.22/) *— 2025 · Proceedings of the Tenth Conference on Machine Translation*
31. [Findings of the WMT25 Multilingual Instruction Shared Task: Persistent Hurdles in Reasoning, Generation, and Evaluation](https://aclanthology.org/2025.wmt-1.23/) *— 2025 · Proceedings of the Tenth Conference on Machine Translation*
32. [Findings of the WMT25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help](https://aclanthology.org/2025.wmt-1.24/) *— 2025 · Proceedings of the Tenth Conference on Machine Translation*
33. [How to Evaluate Speech Translation with Source-Aware Neural MT Metrics](https://arxiv.org/abs/2511.03295) *— 2025 · arXiv:2511.03295*
34. [Investigating Length Issues in Document-level Machine Translation](https://aclanthology.org/2025.mtsummit-1.3/) *— 2025 · Proceedings of Machine Translation Summit XX: Volume 1*
35. [MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators](https://aclanthology.org/2025.coling-main.374/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
36. [MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants](https://aclanthology.org/2025.wmt-1.69/) *— 2025 · Proceedings of the Tenth Conference on Machine Translation*
37. [NHK Submission to WAT 2025: Leveraging Preference Optimization for Article-level Japanese–English News Translation](https://aclanthology.org/2025.wat-1.9/) *— 2025 · Proceedings of the Twelfth Workshop on Asian Translation (WAT 2025)*
38. [The Devil is in the Details: Assessing the Effects of Machine-Translation on LLM Performance in Domain-Specific Texts](https://aclanthology.org/2025.mtsummit-1.24/) *— 2025 · Proceedings of Machine Translation Summit XX: Volume 1*
39. [Towards Zero-Shot Multimodal Machine Translation](https://aclanthology.org/2025.findings-naacl.45/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
40. [TransEvalnia: Reasoning-based Evaluation and Ranking of Translations](https://arxiv.org/abs/2507.12724) *— 2025 · arXiv:2507.12724*
41. [Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation](https://aclanthology.org/2025.acl-long.149/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics](https://aclanthology.org/2025.findings-acl.1175/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
43. [Évaluation de la Robustesse des LLM : Proposition d''un Cadre Méthodologique et Développement d''un Benchmark](https://aclanthology.org/2025.jeptalnrecital-evalllm.13/) *— 2025 · Actes de l'atelier Évaluation des modèles génératifs (LLM) et challenge 2025 (EvalLLM)*
44. [An Analysis on Automated Metrics for Evaluating Japanese-English Chat Translation](https://arxiv.org/abs/2412.18190) *— 2024 · arXiv:2412.18190*
45. [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://md.mova.elamur.ai/free/en/docs/papers/perrella-2024-beyond-correlation-interpretable-evaluation-machine/) *— our card · 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
46. [COMET for Low-Resource Machine Translation Evaluation: A Case Study of English-Maltese and Spanish-Basque](https://aclanthology.org/2024.lrec-main.315/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
47. [DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding](https://aclanthology.org/2024.lrec-main.395/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
48. [Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation](https://aclanthology.org/2024.acl-long.653/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Efficient Benchmarking (of Language Models)](https://aclanthology.org/2024.naacl-long.139/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
50. [Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models](https://aclanthology.org/2024.findings-acl.520/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
51. [Evaluating Automatic Metrics with Incremental Machine Translation Systems](https://aclanthology.org/2024.findings-emnlp.169/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
52. [Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation](https://aclanthology.org/2024.lrec-main.575/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
53. [Finding Blind Spots in Evaluator LLMs with Interpretable Checklists](https://aclanthology.org/2024.emnlp-main.911/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
54. [Findings of the WMT24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet](https://aclanthology.org/2024.wmt-1.1/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
55. [Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy](https://aclanthology.org/2024.wmt-1.118/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
56. [Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis](https://aclanthology.org/2024.wmt-1.127/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
57. [Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages](https://aclanthology.org/2024.naacl-long.170/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
58. [Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies](https://aclanthology.org/2024.acl-long.110/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [Pushing the Limits of Zero-shot End-to-End Speech Translation](https://aclanthology.org/2024.findings-acl.847/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
60. [Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model](https://aclanthology.org/2024.acl-long.836/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Revisiting Meta-evaluation for Grammatical Error Correction](https://aclanthology.org/2024.tacl-1.47/) *— 2024*
62. [Topic-guided Example Selection for Domain Adaptation in LLM-based Machine Translation](https://aclanthology.org/2024.eacl-srw.13/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop*
63. [Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages](https://arxiv.org/abs/2404.02512) *— 2024 · arXiv:2404.02512*
64. [À propos des difficultés de traduire automatiquement de longs documents](https://aclanthology.org/2024.jeptalnrecital-taln.1/) *— 2024 · Actes de la 31ème Conférence sur le Traitement Automatique des Langues Naturelles, volume 1 : articles longs et prises de position*
65. [Adapting Machine Translation Education to the Neural Era: A Case Study of MT Quality Assessment](https://aclanthology.org/2023.eamt-1.30/) *— 2023 · Proceedings of the 24th Annual Conference of the European Association for Machine Translation*
66. [Aligning Offline Metrics and Human Judgments of Value for Code Generation Models](https://aclanthology.org/2023.findings-acl.540/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
67. [Analysing Mistranslation of Emotions in Multilingual Tweets by Online MT Tools](https://aclanthology.org/2023.eamt-1.27/) *— 2023 · Proceedings of the 24th Annual Conference of the European Association for Machine Translation*
68. [BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training](https://aclanthology.org/2023.acl-long.297/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [Beyond Correlation: Making Sense of the Score Differences of New MT Evaluation Metrics](https://aclanthology.org/2023.mtsummit-research.16/) *— 2023 · Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track*
70. [Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation](https://aclanthology.org/2023.acl-long.122/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
71. [Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://aclanthology.org/2023.tacl-1.92/) *— 2023*
72. [Calibrated Interpretation: Confidence Estimation in Semantic Parsing](https://aclanthology.org/2023.tacl-1.69/) *— 2023*
73. [Extrinsic Evaluation of Machine Translation Metrics](https://aclanthology.org/2023.acl-long.730/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [Findings of the 2023 Conference on Machine Translation (WMT23): LLMs Are Here but Not Quite There Yet](https://aclanthology.org/2023.wmt-1.1/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
75. [GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4](https://aclanthology.org/2023.wmt-1.64/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
76. [IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages](https://aclanthology.org/2023.acl-long.795/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [Metric Score Landscape Challenge (MSLC23): Understanding Metrics'' Performance on a Wider Landscape of Translation Quality](https://aclanthology.org/2023.wmt-1.65/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
78. [On the Blind Spots of Model-Based Evaluation Metrics for Text Generation](https://aclanthology.org/2023.acl-long.674/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [PE effort and neural-based automatic MT metrics: do they correlate?](https://aclanthology.org/2023.eamt-1.31/) *— 2023 · Proceedings of the 24th Annual Conference of the European Association for Machine Translation*
80. [Rethinking Round-Trip Translation for Machine Translation Evaluation](https://aclanthology.org/2023.findings-acl.22/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
81. [Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks](https://aclanthology.org/2023.findings-emnlp.340/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
82. [T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics](https://aclanthology.org/2023.findings-emnlp.1014/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
83. [TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation](https://aclanthology.org/2023.acl-short.164/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
84. [The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics](https://aclanthology.org/2023.acl-short.94/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
85. [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*
86. [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)*
87. [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)*
88. [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*
89. [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*
90. [Boosting Code Summarization by Embedding Code Structures](https://aclanthology.org/2022.coling-1.521/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
91. [Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation](https://aclanthology.org/2022.findings-naacl.200/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
92. [Control Prefixes for Parameter-Efficient Text Generation](https://aclanthology.org/2022.gem-1.31/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
93. [DEMETR: Diagnosing Evaluation Metrics for Translation](https://aclanthology.org/2022.emnlp-main.649/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
94. [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*
95. [Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://aclanthology.org/2022.tacl-1.62/) *— 2022 · Transactions of the Association for Computational Linguistics*
96. [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)*
97. [Improving Both Domain Robustness and Domain Adaptability in Machine Translation](https://aclanthology.org/2022.coling-1.461/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
98. [Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics](https://aclanthology.org/2022.findings-acl.14/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
99. [Lack of Fluency is Hurting Your Translation Model](https://arxiv.org/abs/2205.11826) *— 2022 · arXiv:2205.11826*
100. [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)*

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