---
title: "Who cites Bleu: a Method for Automatic Evaluation of…"
description: "The papers of the Elamur Research library that cite «Bleu: a Method for Automatic Evaluation of Machine Translation», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/49/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/49/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Bleu: a Method for Automatic Evaluation of…](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/) / cited by

# Who cites «Bleu: a Method for Automatic Evaluation of Machine Translation» · page 49

10827 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/papineni-2002-bleu-method-automatic-evaluation-machine/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/) · … · [48](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/48/) · **49** · [50](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/50/) · … · [109](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/109/)

1. [Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions](https://aclanthology.org/2022.findings-naacl.106/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
2. [Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks](https://aclanthology.org/2022.emnlp-main.678/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
3. [Improving Jejueo-Korean Translation With Cross-Lingual Pretraining Using Japanese and Korean](https://aclanthology.org/2022.wat-1.3/) *— 2022 · Proceedings of the 9th Workshop on Asian Translation*
4. [Improving Large-scale Paraphrase Acquisition and Generation](https://aclanthology.org/2022.emnlp-main.631/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
5. [Improving Machine Translation with Phrase Pair Injection and Corpus Filtering](https://aclanthology.org/2022.emnlp-main.361/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
6. [Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation](https://aclanthology.org/2022.acl-long.44/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning](https://aclanthology.org/2022.emnlp-main.195/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
8. [Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers](https://aclanthology.org/2022.dlg4nlp-1.2/) *— 2022 · Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)*
9. [Improving Non-Autoregressive Neural Machine Translation via Modeling Localness](https://aclanthology.org/2022.coling-1.463/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
10. [Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation](https://aclanthology.org/2022.findings-emnlp.536/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
11. [Improving Text Simplification with Factuality Error Detection](https://aclanthology.org/2022.tsar-1.16/) *— 2022 · Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)*
12. [Improving Zero-Shot Multilingual Text Generation via Iterative Distillation](https://aclanthology.org/2022.coling-1.513/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
13. [Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mapping](https://aclanthology.org/2022.findings-emnlp.485/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
14. [Improving abstractive summarization with energy-based re-ranking](https://aclanthology.org/2022.gem-1.1/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
15. [Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards](https://aclanthology.org/2022.findings-emnlp.319/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
16. [In-the-Wild Video Question Answering](https://aclanthology.org/2022.coling-1.496/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
17. [Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation](https://aclanthology.org/2022.bionlp-1.10/) *— 2022 · Proceedings of the 21st Workshop on Biomedical Language Processing*
18. [Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective](https://aclanthology.org/2022.emnlp-main.453/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
19. [IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://aclanthology.org/2022.findings-acl.145/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
20. [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://aclanthology.org/2022.emnlp-main.755/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
21. [Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning](https://aclanthology.org/2022.emnlp-main.50/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
22. [Inducing Positive Perspectives with Text Reframing](https://aclanthology.org/2022.acl-long.257/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning](https://aclanthology.org/2022.findings-emnlp.520/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
24. [Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue](https://aclanthology.org/2022.emnlp-main.280/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
25. [Information-Transport-based Policy for Simultaneous Translation](https://aclanthology.org/2022.emnlp-main.65/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
26. [Injecting Domain Knowledge in Language Models for Task-oriented Dialogue Systems](https://aclanthology.org/2022.emnlp-main.820/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
27. [Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?](https://aclanthology.org/2022.wmt-1.15/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
28. [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://aclanthology.org/2022.emnlp-main.33/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
29. [Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach](https://aclanthology.org/2022.acl-srw.6/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop*
30. [Interactive Evaluation of Dialog Track at DSTC9](https://aclanthology.org/2022.lrec-1.616/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
31. [Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation](https://aclanthology.org/2022.ecnlp-1.2/) *— 2022 · Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*
32. [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*
33. [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://aclanthology.org/2022.acl-long.243/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [Investigating Metric Diversity for Evaluating Long Document Summarisation](https://aclanthology.org/2022.sdp-1.13/) *— 2022 · Proceedings of the Third Workshop on Scholarly Document Processing*
35. [Investigating the Benefits of Free-Form Rationales](https://aclanthology.org/2022.findings-emnlp.432/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
36. [Investigating the Impact of Different Pivot Languages on Translation Quality](https://aclanthology.org/2022.amta-wetpr.3/) *— 2022 · Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 1: Empirical Translation Process Research)*
37. [Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples](https://aclanthology.org/2022.emnlp-main.370/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
38. [Investigating the use of Paraphrase Generation for Question Reformulation in the FRANK QA system](https://arxiv.org/abs/2206.02737) *— 2022 · arXiv:2206.02737*
39. [Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation](https://aclanthology.org/2022.wat-1.15/) *— 2022 · Proceedings of the 9th Workshop on Asian Translation*
40. [Investigation of Multilingual Neural Machine Translation for Indian Languages](https://aclanthology.org/2022.wat-1.9/) *— 2022 · Proceedings of the 9th Workshop on Asian Translation*
41. [IrekiaLFes: a New Open Benchmark and Baseline Systems for Spanish Automatic Text Simplification](https://aclanthology.org/2022.tsar-1.8/) *— 2022 · Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)*
42. [Is Encoder-Decoder Redundant for Neural Machine Translation?](https://aclanthology.org/2022.aacl-main.43/) *— 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)*
43. [Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://aclanthology.org/2022.acl-long.501/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://arxiv.org/abs/2210.01241) *— 2022 · arXiv:2210.01241*
45. [JADES: New Text Simplification Dataset in Japanese Targeted at Non-Native Speakers](https://aclanthology.org/2022.tsar-1.17/) *— 2022 · Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)*
46. [JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation](https://aclanthology.org/2022.emnlp-main.550/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
47. [JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization](https://aclanthology.org/2022.emnlp-main.825/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
48. [JHU IWSLT 2022 Dialect Speech Translation System Description](https://aclanthology.org/2022.iwslt-1.29/) *— 2022 · Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)*
49. [JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation](https://aclanthology.org/2022.coling-1.523/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
50. [JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus](https://aclanthology.org/2022.lrec-1.721/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
51. [JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT](https://aclanthology.org/2022.emnlp-demos.6/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
52. [Jojajovai: A Parallel Guarani-Spanish Corpus for MT Benchmarking](https://aclanthology.org/2022.lrec-1.226/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
53. [KC4MT: A High-Quality Corpus for Multilingual Machine Translation](https://aclanthology.org/2022.lrec-1.588/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
54. [KHANQ: A Dataset for Generating Deep Questions in Education](https://aclanthology.org/2022.coling-1.518/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
55. [KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding](https://aclanthology.org/2022.findings-acl.30/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
56. [Keep Me Updated! Memory Management in Long-term Conversations](https://aclanthology.org/2022.findings-emnlp.276/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
57. [Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation](https://aclanthology.org/2022.acl-long.304/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Klexikon: A German Dataset for Joint Summarization and Simplification](https://aclanthology.org/2022.lrec-1.288/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
59. [Know Better – A Clickbait Resolving Challenge](https://aclanthology.org/2022.lrec-1.54/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
60. [KnowPAML:A Knowledge Enhanced Framework for Adaptable Personalized Dialogue Generation Using Meta-Learning](https://aclanthology.org/2022.icon-main.25/) *— 2022 · Proceedings of the 19th International Conference on Natural Language Processing (ICON)*
61. [Knowledge Distillation for Sustainable Neural Machine Translation](https://aclanthology.org/2022.amta-upg.16/) *— 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)*
62. [Knowledge Enhanced Reflection Generation for Counseling Dialogues](https://aclanthology.org/2022.acl-long.221/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation](https://aclanthology.org/2022.tu-1.2/) *— 2022 · Proceedings of the First Workshop On Transcript Understanding*
64. [Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks](https://aclanthology.org/2022.sigdial-1.3/) *— 2022 · Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
65. [Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation](https://aclanthology.org/2022.naacl-main.15/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
66. [LARD: Large-scale Artificial Disfluency Generation](https://aclanthology.org/2022.lrec-1.249/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
67. [LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue](https://aclanthology.org/2022.findings-aacl.4/) *— 2022 · Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022*
68. [LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases](https://aclanthology.org/2022.coling-1.572/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
69. [LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation](https://aclanthology.org/2022.tacl-1.25/) *— 2022*
70. [LaMDA: Language Models for Dialog Applications](https://arxiv.org/abs/2201.08239) *— 2022 · arXiv:2201.08239*
71. [Lack of Fluency is Hurting Your Translation Model](https://arxiv.org/abs/2205.11826) *— 2022 · arXiv:2205.11826*
72. [Lan-Bridge MT's Participation in the WMT 2022 General Translation Shared Task](https://aclanthology.org/2022.wmt-1.19/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
73. [Language Branch Gated Multilingual Neural Machine Translation](https://aclanthology.org/2022.coling-1.447/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
74. [Language Invariant Properties in Natural Language Processing](https://aclanthology.org/2022.nlppower-1.9/) *— 2022 · Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP*
75. [Latent Group Dropout for Multilingual and Multidomain Machine Translation](https://aclanthology.org/2022.findings-naacl.189/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
76. [Layer or Representation Space: What Makes BERT-based Evaluation Metrics Robust?](https://aclanthology.org/2022.coling-1.300/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
77. [Learning Confidence for Transformer-based Neural Machine Translation](https://aclanthology.org/2022.acl-long.167/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
78. [Learning Interpretable Latent Dialogue Actions With Less Supervision](https://aclanthology.org/2022.aacl-main.24/) *— 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)*
79. [Learning Natural Language Generation with Truncated Reinforcement Learning](https://aclanthology.org/2022.naacl-main.2/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
80. [Learning Structural Information for Syntax-Controlled Paraphrase Generation](https://aclanthology.org/2022.findings-naacl.160/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
81. [Learning When to Translate for Streaming Speech](https://aclanthology.org/2022.acl-long.50/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
82. [Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer](https://aclanthology.org/2022.findings-naacl.201/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
83. [Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference](https://aclanthology.org/2022.findings-acl.119/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
84. [Learning to Adapt to Low-Resource Paraphrase Generation](https://aclanthology.org/2022.emnlp-main.66/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
85. [Learning to Describe Solutions for Bug Reports Based on Developer Discussions](https://aclanthology.org/2022.findings-acl.231/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
86. [Learning to Embed Multi-Modal Contexts for Situated Conversational Agents](https://aclanthology.org/2022.findings-naacl.61/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
87. [Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation](https://aclanthology.org/2022.acl-long.546/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Learning to Rank Visual Stories From Human Ranking Data](https://aclanthology.org/2022.acl-long.441/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
89. [Learning to Transfer Prompts for Text Generation](https://aclanthology.org/2022.naacl-main.257/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
90. [Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback](https://aclanthology.org/2022.findings-naacl.26/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
91. [Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://aclanthology.org/2022.naacl-main.426/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
92. [Less is More: Summary of Long Instructions is Better for Program Synthesis](https://aclanthology.org/2022.emnlp-main.301/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
93. [Lexical Knowledge Internalization for Neural Dialog Generation](https://aclanthology.org/2022.acl-long.547/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
94. [Linearizing Transformer with Key-Value Memory](https://aclanthology.org/2022.emnlp-main.24/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
95. [Linguistic Corpus Annotation for Automatic Text Simplification Evaluation](https://aclanthology.org/2022.emnlp-main.121/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
96. [Linguistically Motivated Evaluation of Machine Translation Metrics Based on a Challenge Set](https://aclanthology.org/2022.wmt-1.45/) *— 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
97. [Linguistically-Motivated Yorùbá-English Machine Translation](https://aclanthology.org/2022.coling-1.449/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
98. [Local Structure Matters Most: Perturbation Study in NLU](https://aclanthology.org/2022.findings-acl.293/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
99. [Logic-Guided Message Generation from Raw Real-Time Sensor Data](https://aclanthology.org/2022.lrec-1.745/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
100. [Long Text Generation with Topic-aware Discrete Latent Variable Model](https://aclanthology.org/2022.emnlp-main.554/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*

Pages: [1](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/) · … · [48](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/48/) · **49** · [50](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/50/) · … · [109](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/cited-by/page/109/)