---
title: "Who cites A Call for Clarity in Reporting BLEU Scores —…"
description: "The papers of the Elamur Research library that cite «A Call for Clarity in Reporting BLEU Scores», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/cited-by/page/20/"
---
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# Who cites «A Call for Clarity in Reporting BLEU Scores» · page 20

2105 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/post-2018-call-clarity-reporting-bleu-scores/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/cited-by/) · … · [19](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/cited-by/page/19/) · **20** · [21](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/cited-by/page/21/) · [22](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/cited-by/page/22/)

1. [HAT: Hardware-Aware Transformers for Efficient Natural Language Processing](https://aclanthology.org/2020.acl-main.686/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
2. [HW-TSC's Participation in the WAT 2020 Indic Languages Multilingual Task](https://aclanthology.org/2020.wat-1.8/) *— 2020 · Proceedings of the 7th Workshop on Asian Translation*
3. [Hard-Coded Gaussian Attention for Neural Machine Translation](https://aclanthology.org/2020.acl-main.687/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
4. [How Should Markup Tags Be Translated?](https://aclanthology.org/2020.wmt-1.138/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
5. [Human-Paraphrased References Improve Neural Machine Translation](https://aclanthology.org/2020.wmt-1.140/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
6. [IIE's Neural Machine Translation Systems for WMT20](https://aclanthology.org/2020.wmt-1.32/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
7. [If You Build Your Own NER Scorer, Non-replicable Results Will Come](https://aclanthology.org/2020.insights-1.15/) *— 2020 · Proceedings of the First Workshop on Insights from Negative Results in NLP*
8. [If beam search is the answer, what was the question?](https://aclanthology.org/2020.emnlp-main.170/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
9. [Imitation Attacks and Defenses for Black-box Machine Translation Systems](https://aclanthology.org/2020.emnlp-main.446/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
10. [Improving Fluency of Non-Autoregressive Machine Translation](https://arxiv.org/abs/2004.03227) *— 2020 · arXiv:2004.03227*
11. [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://aclanthology.org/2020.acl-main.148/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
12. [Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.319/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
13. [Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition](https://aclanthology.org/2020.findings-emnlp.250/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
14. [In Neural Machine Translation, What Does Transfer Learning Transfer?](https://aclanthology.org/2020.acl-main.688/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
15. [Inference Strategies for Machine Translation with Conditional Masking](https://aclanthology.org/2020.emnlp-main.465/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
16. [Inference-only sub-character decomposition improves translation of unseen logographic characters](https://aclanthology.org/2020.wat-1.21/) *— 2020 · Proceedings of the 7th Workshop on Asian Translation*
17. [It”s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information](https://aclanthology.org/2020.acl-main.149/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
18. [It”s not a Non-Issue: Negation as a Source of Error in Machine Translation](https://aclanthology.org/2020.findings-emnlp.345/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
19. [It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations](https://aclanthology.org/2020.acl-main.263/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
20. [Iterative Domain-Repaired Back-Translation](https://aclanthology.org/2020.emnlp-main.474/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
21. [Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.73/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
22. [JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus](https://aclanthology.org/2020.lrec-1.443/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
23. [LIMSI @ WMT 2020](https://aclanthology.org/2020.wmt-1.86/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
24. [Language Model Prior for Low-Resource Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.615/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
25. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
26. [Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling](https://aclanthology.org/2020.wmt-1.69/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
27. [Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?](https://aclanthology.org/2020.findings-emnlp.390/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
28. [Learning a Multi-Domain Curriculum for Neural Machine Translation](https://aclanthology.org/2020.acl-main.689/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
29. [Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation](https://aclanthology.org/2020.acl-main.252/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
30. [Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting](https://aclanthology.org/2020.ngt-1.11/) *— 2020 · Proceedings of the Fourth Workshop on Neural Generation and Translation*
31. [Lite Training Strategies for Portuguese-English and English-Portuguese Translation](https://aclanthology.org/2020.wmt-1.90/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
32. [Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.211/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
33. [Machine Translation System Selection from Bandit Feedback](https://aclanthology.org/2020.amta-research.5/) *— 2020 · Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*
34. [Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?](https://aclanthology.org/2020.tacl-1.4/) *— 2020 · Transactions of the Association for Computational Linguistics*
35. [Meta-Learning for Few-Shot NMT Adaptation](https://aclanthology.org/2020.ngt-1.5/) *— 2020 · Proceedings of the Fourth Workshop on Neural Generation and Translation*
36. [Mitigating Gender Bias in Machine Translation with Target Gender Annotations](https://aclanthology.org/2020.wmt-1.73/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
37. [Multi-task Learning for Multilingual Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.75/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
38. [Multilingual AMR-to-Text Generation](https://aclanthology.org/2020.emnlp-main.231/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
39. [Multilingual Neural Machine Translation involving Indian Languages](https://aclanthology.org/2020.wildre-1.6/) *— 2020 · Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation*
40. [NICE: Neural Integrated Custom Engines](https://aclanthology.org/2020.eamt-1.35/) *— 2020 · Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*
41. [NRC Systems for the 2020 Inuktitut-English News Translation Task](https://aclanthology.org/2020.wmt-1.13/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
42. [Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It](https://aclanthology.org/2020.gebnlp-1.4/) *— 2020 · Proceedings of the Second Workshop on Gender Bias in Natural Language Processing*
43. [Neural Machine Translation between similar South-Slavic languages](https://aclanthology.org/2020.wmt-1.51/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
44. [Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara](https://aclanthology.org/2020.loresmt-1.3/) *— 2020 · Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages*
45. [Neural Machine Translation for translating into Croatian and Serbian](https://aclanthology.org/2020.vardial-1.10/) *— 2020 · Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects*
46. [Neural Machine Translation with Monte-Carlo Tree Search](https://arxiv.org/abs/2004.12527) *— 2020 · arXiv:2004.12527*
47. [Neural Transduction for Multilingual Lexical Translation](https://aclanthology.org/2020.coling-main.387/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
48. [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://aclanthology.org/2020.emnlp-main.207/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
49. [OPPO's Machine Translation System for the IWSLT 2020 Open Domain Translation Task](https://aclanthology.org/2020.iwslt-1.13/) *— 2020 · Proceedings of the 17th International Conference on Spoken Language Translation*
50. [OPPO's Machine Translation Systems for WMT20](https://aclanthology.org/2020.wmt-1.30/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
51. [On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation](https://aclanthology.org/2020.acl-main.326/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
52. [On Long-Tailed Phenomena in Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.276/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
53. [On Romanization for Model Transfer Between Scripts in Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.223/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
54. [On The Evaluation of Machine Translation Systems Trained With Back-Translation](https://aclanthology.org/2020.acl-main.253/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
55. [On the differences between human translations](https://aclanthology.org/2020.eamt-1.39/) *— 2020 · Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*
56. [Optimizing Transformer for Low-Resource Neural Machine Translation](https://aclanthology.org/2020.coling-main.304/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
57. [ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT”20 Metrics Shared Task](https://aclanthology.org/2020.wmt-1.98/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
58. [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://aclanthology.org/2020.lrec-1.465/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
59. [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://aclanthology.org/2020.findings-emnlp.195/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
60. [Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information](https://aclanthology.org/2020.emnlp-main.210/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
61. [Pre-training via Paraphrasing](https://arxiv.org/abs/2006.15020) *— 2020 · arXiv:2006.15020*
62. [Predicting and Using Target Length in Neural Machine Translation](https://aclanthology.org/2020.aacl-main.41/) *— 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing*
63. [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://aclanthology.org/2020.acl-main.690/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
64. [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) *— 2020 · arXiv:2001.04451*
65. [Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference](https://aclanthology.org/2020.emnlp-main.17/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
66. [Response Selection for Multi-Party Conversations with Dynamic Topic Tracking](https://aclanthology.org/2020.emnlp-main.533/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
67. [Results of the WMT20 Metrics Shared Task](https://aclanthology.org/2020.wmt-1.77/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
68. [Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT](https://aclanthology.org/2020.emnlp-main.214/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
69. [Revisiting Modularized Multilingual NMT to Meet Industrial Demands](https://aclanthology.org/2020.emnlp-main.476/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
70. [Revisiting Round-trip Translation for Quality Estimation](https://aclanthology.org/2020.eamt-1.11/) *— 2020 · Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*
71. [Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training](https://aclanthology.org/2020.coling-main.374/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
72. [Russian-English Bidirectional Machine Translation System](https://aclanthology.org/2020.wmt-1.35/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
73. [SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation](https://aclanthology.org/2020.emnlp-demos.19/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
74. [SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task](https://aclanthology.org/2020.wmt-1.22/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
75. [Self-Attention with Cross-Lingual Position Representation](https://aclanthology.org/2020.acl-main.153/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
76. [Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation](https://arxiv.org/abs/2011.03286) *— 2020 · arXiv:2011.03286*
77. [SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation](https://aclanthology.org/2020.aacl-main.58/) *— 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing*
78. [Simulated multiple reference training improves low-resource machine translation](https://aclanthology.org/2020.emnlp-main.7/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
79. [Simultaneous Machine Translation with Visual Context](https://aclanthology.org/2020.emnlp-main.184/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
80. [Sockeye 2: A Toolkit for Neural Machine Translation](https://aclanthology.org/2020.eamt-1.50/) *— 2020 · Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*
81. [SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP](https://aclanthology.org/2020.coling-main.418/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
82. [Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task](https://aclanthology.org/2020.wmt-1.17/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
83. [Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation](https://aclanthology.org/2020.wmt-1.64/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
84. [Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture](https://aclanthology.org/2020.acl-main.360/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
85. [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://md.research.elamur.ai/free/en/papers/mathur-2020-tangled-up-bleu-reevaluating-evaluation/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
86. [TeaForN: Teacher-Forcing with N-grams](https://aclanthology.org/2020.emnlp-main.702/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
87. [Tensorized Embedding Layers](https://aclanthology.org/2020.findings-emnlp.436/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
88. [The ADAPT System Description for the WMT20 News Translation Task](https://aclanthology.org/2020.wmt-1.27/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
89. [The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.327/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
90. [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*
91. [The DeepMind Chinese–English Document Translation System at WMT2020](https://aclanthology.org/2020.wmt-1.36/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
92. [The EOS Decision and Length Extrapolation](https://aclanthology.org/2020.blackboxnlp-1.26/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
93. [The FISKMÖ Project: Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research](https://aclanthology.org/2020.lrec-1.470/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
94. [The IPN-CIC team system submission for the WMT 2020 similar language task](https://aclanthology.org/2020.wmt-1.47/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
95. [The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task](https://aclanthology.org/2020.wmt-1.128/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
96. [The NiuTrans System for WNGT 2020 Efficiency Task](https://aclanthology.org/2020.ngt-1.24/) *— 2020 · Proceedings of the Fourth Workshop on Neural Generation and Translation*
97. [The Nunavut Hansard Inuktitut–English Parallel Corpus 3.0 with Preliminary Machine Translation Results](https://aclanthology.org/2020.lrec-1.312/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
98. [The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020](https://aclanthology.org/2020.amta-research.10/) *— 2020 · Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*
99. [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
100. [The Ubiqus English-Inuktitut System for WMT20](https://aclanthology.org/2020.wmt-1.21/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*

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