[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-12/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «A Call for Clarity in Reporting BLEU Scores» · page 12

# Who cites «A Call for Clarity in Reporting BLEU Scores» · page 12

2102 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/post-2018-call-clarity-reporting-bleu-scores/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-7/) · [8](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-8/) · [9](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-9/) · [10](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-10/) · [11](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-11/) · **12** · [13](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-13/) · [14](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-14/) · [15](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-15/) · [16](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-16/) · [17](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-17/) · [18](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-18/) · [19](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-19/) · [20](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-20/) · [21](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-21/) · [22](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-22/)

1. [Improving Zero-Shot Translation by Disentangling Positional Information](https://aclanthology.org/2021.acl-long.101/) *— 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)*
2. [Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation](https://aclanthology.org/2021.naacl-main.16/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
3. [Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation](https://aclanthology.org/2021.emnlp-main.666/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
4. [Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021](https://aclanthology.org/2021.wat-1.3/) *— 2021 · Proceedings of the 8th Workshop on Asian Translation (WAT2021)*
5. [Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages](https://aclanthology.org/2021.mtsummit-research.7/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
6. [Is ``moby dick'' a Whale or a Bird? Named Entities and Terminology in Speech Translation](https://aclanthology.org/2021.emnlp-main.128/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
7. [Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse](https://aclanthology.org/2021.woah-1.14/) *— 2021 · Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)*
8. [Joint Optimization of Tokenization and Downstream Model](https://aclanthology.org/2021.findings-acl.21/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
9. [LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer](https://aclanthology.org/2021.findings-acl.344/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
10. [LISN @ WMT 2021](https://aclanthology.org/2021.wmt-1.22/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
11. [Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning](https://aclanthology.org/2021.eacl-main.233/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
12. [Learning Kernel-Smoothed Machine Translation with Retrieved Examples](https://aclanthology.org/2021.emnlp-main.579/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
13. [Learning to Rewrite for Non-Autoregressive Neural Machine Translation](https://aclanthology.org/2021.emnlp-main.265/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
14. [Learning to Selectively Learn for Weakly-supervised Paraphrase Generation](https://aclanthology.org/2021.emnlp-main.480/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
15. [Linear Transformers Are Secretly Fast Weight Programmers](https://arxiv.org/abs/2102.11174) *— 2021 · arXiv:2102.11174*
16. [MMTAfrica: Multilingual Machine Translation for African Languages](https://aclanthology.org/2021.wmt-1.48/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
17. [Machine Translation into Low-resource Language Varieties](https://aclanthology.org/2021.acl-short.16/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
18. [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://aclanthology.org/2021.eacl-main.188/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
19. [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://aclanthology.org/2021.acl-demo.37/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations*
20. [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://aclanthology.org/2021.acl-long.505/) *— 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)*
21. [Modeling Bilingual Conversational Characteristics for Neural Chat Translation](https://aclanthology.org/2021.acl-long.444/) *— 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)*
22. [Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model](https://aclanthology.org/2021.findings-emnlp.121/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
23. [Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation](https://aclanthology.org/2021.acl-short.46/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
24. [Monotonic Simultaneous Translation with Chunk-wise Reordering and Refinement](https://aclanthology.org/2021.wmt-1.119/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
25. [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://aclanthology.org/2021.emnlp-main.54/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
26. [Morphologically-Guided Segmentation For Translation of Agglutinative Low-Resource Languages](https://aclanthology.org/2021.mtsummit-loresmt.3/) *— 2021 · Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)*
27. [Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging](https://aclanthology.org/2021.mtsummit-up.27/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Users and Providers Track*
28. [Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning](https://aclanthology.org/2021.metanlp-1.4/) *— 2021 · Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing*
29. [Multi-Pair Text Style Transfer on Unbalanced Data](https://arxiv.org/abs/2106.10608) *— 2021 · arXiv:2106.10608*
30. [MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents](https://aclanthology.org/2021.emnlp-main.498/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
31. [Multilingual Agreement for Multilingual Neural Machine Translation](https://aclanthology.org/2021.acl-short.31/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
32. [Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters](https://aclanthology.org/2021.wmt-1.64/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
33. [Multilingual Speech Translation from Efficient Finetuning of Pretrained Models](https://aclanthology.org/2021.acl-long.68/) *— 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)*
34. [Multilingual Translation via Grafting Pre-trained Language Models](https://aclanthology.org/2021.findings-emnlp.233/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
35. [Multilingual Unsupervised Neural Machine Translation with Denoising Adapters](https://aclanthology.org/2021.emnlp-main.533/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
36. [Multimodal Neural Machine Translation System for English to Bengali](https://aclanthology.org/2021.mmtlrl-1.6/) *— 2021 · Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)*
37. [Mutual-Learning Improves End-to-End Speech Translation](https://aclanthology.org/2021.emnlp-main.325/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
38. [NICT Kyoto Submission for the WMT''21 Quality Estimation Task: Multimetric Multilingual Pretraining for Critical Error Detection](https://aclanthology.org/2021.wmt-1.99/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
39. [NLPHut's Participation at WAT2021](https://aclanthology.org/2021.wat-1.16/) *— 2021 · Proceedings of the 8th Workshop on Asian Translation (WAT2021)*
40. [NRC-CNRC Machine Translation Systems for the 2021 AmericasNLP Shared Task](https://aclanthology.org/2021.americasnlp-1.25/) *— 2021 · Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas*
41. [NRC-CNRC Systems for Upper Sorbian-German and Lower Sorbian-German Machine Translation 2021](https://aclanthology.org/2021.wmt-1.107/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
42. [Neural Machine Translation Quality and Post-Editing Performance](https://aclanthology.org/2021.emnlp-main.801/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
43. [Neural Machine Translation in Low-Resource Setting: a Case Study in English-Marathi Pair](https://aclanthology.org/2021.mtsummit-research.4/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
44. [Neural Machine Translation with Inflected Lexicon](https://aclanthology.org/2021.mtsummit-research.23/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
45. [Neural-Driven Search-Based Paraphrase Generation](https://aclanthology.org/2021.eacl-main.180/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
46. [NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation](https://aclanthology.org/2021.wmt-1.108/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
47. [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://aclanthology.org/2021.eacl-main.259/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
48. [Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models](https://aclanthology.org/2021.wnut-1.23/) *— 2021 · Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)*
49. [OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning](https://aclanthology.org/2021.eacl-demos.34/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations*
50. [OTTers: One-turn Topic Transitions for Open-Domain Dialogue](https://aclanthology.org/2021.acl-long.194/) *— 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)*
51. [Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://aclanthology.org/2021.naacl-main.256/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
52. [On Biasing Transformer Attention Towards Monotonicity](https://aclanthology.org/2021.naacl-main.354/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
53. [On Compositional Generalization of Neural Machine Translation](https://aclanthology.org/2021.acl-long.368/) *— 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)*
54. [On Sparsifying Encoder Outputs in Sequence-to-Sequence Models](https://aclanthology.org/2021.findings-acl.255/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
55. [On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation](https://aclanthology.org/2021.findings-emnlp.247/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
56. [On the Copying Behaviors of Pre-Training for Neural Machine Translation](https://aclanthology.org/2021.findings-acl.373/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
57. [On the Language Coverage Bias for Neural Machine Translation](https://aclanthology.org/2021.findings-acl.422/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
58. [ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair](https://aclanthology.org/2021.paclic-1.56/) *— 2021 · Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation*
59. [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://aclanthology.org/2021.tacl-1.68/) *— 2021 · Transactions of the Association for Computational Linguistics*
60. [PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation](https://aclanthology.org/2021.emnlp-main.369/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
61. [Promoting Graph Awareness in Linearized Graph-to-Text Generation](https://aclanthology.org/2021.findings-acl.82/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
62. [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) *— 2021 · arXiv:2102.07350*
63. [Pruning Neural Machine Translation for Speed Using Group Lasso](https://aclanthology.org/2021.wmt-1.116/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
64. [Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation](https://aclanthology.org/2021.naacl-main.308/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
65. [Putting words into the system''s mouth: A targeted attack on neural machine translation using monolingual data poisoning](https://aclanthology.org/2021.findings-acl.127/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
66. [Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation](https://aclanthology.org/2021.eacl-main.301/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
67. [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*
68. [Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach](https://aclanthology.org/2021.emnlp-main.669/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
69. [Rethinking Perturbations in Encoder-Decoders for Fast Training](https://aclanthology.org/2021.naacl-main.460/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
70. [Revisiting Negation in Neural Machine Translation](https://aclanthology.org/2021.tacl-1.45/) *— 2021 · Transactions of the Association for Computational Linguistics*
71. [Revisiting Robust Neural Machine Translation: A Transformer Case Study](https://aclanthology.org/2021.findings-emnlp.323/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
72. [Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation](https://aclanthology.org/2021.naacl-main.133/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
73. [Rule-based Morphological Inflection Improves Neural Terminology Translation](https://aclanthology.org/2021.emnlp-main.477/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
74. [SHAPE: Shifted Absolute Position Embedding for Transformers](https://aclanthology.org/2021.emnlp-main.266/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
75. [SLTEV: Comprehensive Evaluation of Spoken Language Translation](https://aclanthology.org/2021.eacl-demos.9/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations*
76. [SYSTRAN @ WMT 2021: Terminology Task](https://aclanthology.org/2021.wmt-1.84/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
77. [Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers](https://aclanthology.org/2021.acl-long.566/) *— 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)*
78. [Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks](https://aclanthology.org/2021.naacl-main.151/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
79. [Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation](https://aclanthology.org/2021.acl-long.221/) *— 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)*
80. [Semantic Similarity Based Evaluation for Abstractive News Summarization](https://aclanthology.org/2021.gem-1.3/) *— 2021 · Proceedings of the First Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
81. [Sentiment-Aware Measure (SAM) for Evaluating Sentiment Transfer by Machine Translation Systems](https://aclanthology.org/2021.ranlp-1.137/) *— 2021 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)*
82. [SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation](https://aclanthology.org/2021.eval4nlp-1.5/) *— 2021 · Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems*
83. [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://aclanthology.org/2021.gem-1.4/) *— 2021 · Proceedings of the First Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
84. [Simultaneous Speech Translation for Live Subtitling: from Delay to Display](https://aclanthology.org/2021.mtsummit-asltrw.4/) *— 2021 · Proceedings of the 1st Workshop on Automatic Spoken Language Translation in Real-World Settings (ASLTRW)*
85. [Smoothing and Shrinking the Sparse Seq2Seq Search Space](https://aclanthology.org/2021.naacl-main.210/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
86. [Sparse Attention with Linear Units](https://aclanthology.org/2021.emnlp-main.523/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
87. [Speechformer: Reducing Information Loss in Direct Speech Translation](https://aclanthology.org/2021.emnlp-main.127/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
88. [Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders](https://aclanthology.org/2021.acl-long.204/) *— 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)*
89. [Streaming Models for Joint Speech Recognition and Translation](https://aclanthology.org/2021.eacl-main.216/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
90. [Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages](https://aclanthology.org/2021.mtsummit-loresmt.6/) *— 2021 · Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)*
91. [Subword Mapping and Anchoring across Languages](https://aclanthology.org/2021.findings-emnlp.224/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
92. [Surprise Language Challenge: Developing a Neural Machine Translation System between Pashto and English in Two Months](https://aclanthology.org/2021.mtsummit-research.8/) *— 2021 · Proceedings of Machine Translation Summit XVIII: Research Track*
93. [Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation](https://aclanthology.org/2021.bucc-1.4/) *— 2021 · Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)*
94. [TellMeWhy: A Dataset for Answering Why-Questions in Narratives](https://md.mova.elamur.ai/free/en/docs/papers/lal-2021-tellmewhy-dataset-answering-why-questions/) *— our card · 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
95. [Ten Years of BabelNet: A Survey](https://www.ijcai.org/proceedings/2021/620) *— 2021 · IJCAI-21*
96. [TenTrans High-Performance Inference Toolkit for WMT2021 Efficiency Task](https://aclanthology.org/2021.wmt-1.77/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
97. [TenTrans Large-Scale Multilingual Machine Translation System for WMT21](https://aclanthology.org/2021.wmt-1.53/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
98. [TermMind: Alibaba''s WMT21 Machine Translation Using Terminologies Task Submission](https://aclanthology.org/2021.wmt-1.85/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
99. [TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling](https://aclanthology.org/2021.acl-long.293/) *— 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)*
100. [The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification](https://aclanthology.org/2021.cl-4.28/) *— 2021 · Computational Linguistics*

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-7/) · [8](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-8/) · [9](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-9/) · [10](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-10/) · [11](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-11/) · **12** · [13](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-13/) · [14](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-14/) · [15](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-15/) · [16](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-16/) · [17](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-17/) · [18](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-18/) · [19](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-19/) · [20](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-20/) · [21](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-21/) · [22](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-22/)

- [← previous Who cites «A Call for Clarity in Reporting BLEU Scores» · page 11](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-11/)
- [next → Who cites «A Call for Clarity in Reporting BLEU Scores» · page 13](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/post-2018-call-clarity-reporting-bleu-scores-13/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)