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# Who cites «A Structured Review of the Validity of BLEU» · page 2

162 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/reiter-2018-structured-review-validity-bleu/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/reiter-2018-structured-review-validity-bleu/) · **2**

1. [ReproGen: Proposal for a Shared Task on Reproducibility of Human Evaluations in NLG](https://aclanthology.org/2020.inlg-1.29/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
2. [SportSett:Basketball - A robust and maintainable data-set for Natural Language Generation](https://aclanthology.org/2020.intellang-1.4/) *— 2020 · Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation*
3. [Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text](https://aclanthology.org/2020.inlg-1.6/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
4. [Syntax-Guided Controlled Generation of Paraphrases](https://aclanthology.org/2020.tacl-1.22/) *— 2020 · Transactions of the Association for Computational Linguistics*
5. [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://md.mova.elamur.ai/free/en/docs/papers/mathur-2020-tangled-up-bleu-reevaluating-evaluation/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
6. [The Amazing World of Neural Language Generation](https://aclanthology.org/2020.emnlp-tutorials.7/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
7. [The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation](https://aclanthology.org/2020.inlg-1.10/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
8. [Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions](https://aclanthology.org/2020.inlg-1.23/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
9. [A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation](https://aclanthology.org/P19-1256/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
10. [Agreement is overrated: A plea for correlation to assess human evaluation reliability](https://aclanthology.org/W19-8642/) *— 2019 · Proceedings of the 12th International Conference on Natural Language Generation*
11. [Analyzing Sentence Fusion in Abstractive Summarization](https://aclanthology.org/D19-5413/) *— 2019 · Proceedings of the 2nd Workshop on New Frontiers in Summarization*
12. [Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)](https://aclanthology.org/W19-8644/) *— 2019 · Proceedings of the 12th International Conference on Natural Language Generation*
13. [Best practices for the human evaluation of automatically generated text](https://aclanthology.org/W19-8643/) *— 2019 · Proceedings of the 12th International Conference on Natural Language Generation*
14. [Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue](https://aclanthology.org/P19-1080/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
15. [Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite](https://aclanthology.org/D19-1294/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
16. [Handling Divergent Reference Texts when Evaluating Table-to-Text Generation](https://aclanthology.org/P19-1483/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
17. [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://aclanthology.org/D19-1053/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
18. [Neural Generation for Czech: Data and Baselines](https://aclanthology.org/W19-8670/) *— 2019 · Proceedings of the 12th International Conference on Natural Language Generation*
19. [Neural Text Summarization: A Critical Evaluation](https://aclanthology.org/D19-1051/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
20. [Towards Comprehensive Description Generation from Factual Attribute-value Tables](https://aclanthology.org/P19-1600/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
21. [A Call for Clarity in Reporting BLEU Scores](https://md.mova.elamur.ai/free/en/docs/papers/post-2018-call-clarity-reporting-bleu-scores/) *— our card · 2018 · Proceedings of the Third Conference on Machine Translation: Research Papers*
22. [Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods](https://aclanthology.org/W18-6504/) *— 2018 · Proceedings of the 11th International Conference on Natural Language Generation*
23. [Evaluation methodologies in Automatic Question Generation 2013-2018](https://aclanthology.org/W18-6537/) *— 2018 · Proceedings of the 11th International Conference on Natural Language Generation*
24. aufrant-2022-is-nlp-ready-standardization
25. bhargava-2023-decomposed-scoring-ccg-dependencies
26. bonaventura-2025-detection-explanation-effective-learning-strategies
27. braggaar-2026-evaluating-task-oriented-dialogue-systems
28. clinciu-2021-it-s-commonsense-isn-t
29. corbelle-2024-reprohum-0927-3-reproducing-human
30. donati-2025-do-large-language-models-understand
31. dyer-2019-weighted-posets-learning-surface-order
32. emmerling-2026-evaluating-large-language-model-based
33. gabburo-2023-square-automatic-question-answering-evaluation
34. guanilo-2025-ec-tab2text-aspect-based-text
35. hamalainen-2025-nlg-evaluation-modern-student-assessment
36. harvey-2025-understanding-meeting-practitioner-needs-when
37. huang-2025-do-robot-snakes-dream-like
38. huidrom-2025-dcu-adapt-modpb-gem-24
39. kasner-2026-llms-as-span-annotators-comparative
40. khan-2026-stoicllm-preference-optimization-philosophical-alignment
41. kumar-2026-pressure-testing-deception-probes-llms
42. li-2026-matcha-matching-text-contrastive-semantic
43. liu-2025-quasar-question-driven-structure-aware
44. loakman-2024-reprohum-0087-01-human-evaluation
45. mahapatra-2021-exploring-structural-encoding-data-text
46. mariotti-2020-harnessing-natural-language-generation-explain
47. miltenburg-2020-evaluation-rules-use-grammars-rule
48. miltenburg-2020-how-do-image-description-systems
49. mu-2022-universal-evasion-attacks-summarization-scoring
50. murtaza-2025-implementing-retrieval-augmented-generation-technique
51. naik-2025-crscore-grounding-automated-evaluation-code
52. naskar-2019-text-embellishment-attention-based-encoder
53. nyberg-2021-estimating-subjective-crowd-evaluations-as
54. park-2025-can-llms-help-uncover-insights
55. raha-2026-cross-examination-framework-task-agnostic
56. rebuffel-2020-parenting-model-agnostic-reinforcement-learning
57. sarzotti-2025-mllms-construction-company-investigating-multimodal
58. scalvini-2025-prompt-engineering-enhances-faroese-mt
59. schmidtova-2024-faithfulness-natural-language-generation
60. song-2024-context-aware-visual-storytelling-visual
61. upadhyay-2022-content-type-profiling-data-text
62. ye-2024-tram-token-level-retrieval-augmented

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