---
title: "Who cites Curriculum Learning — page 2"
description: "The papers of the Elamur Research library that cite «Curriculum Learning», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/cited-by/page/2/"
---
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# Who cites «Curriculum Learning» · page 2

659 papers of our library cite it, the most cited first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/cited-by/page/3/) · … · [7](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/cited-by/page/7/)

1. [Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation](https://aclanthology.org/W17-3209/) *— 2017 · Proceedings of the First Workshop on Neural Machine Translation*
2. [The Grammar-Learning Trajectories of Neural Language Models](https://aclanthology.org/2022.acl-long.568/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Fine-Grained Propaganda Detection with Fine-Tuned BERT](https://aclanthology.org/D19-5011/) *— 2019 · Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda*
4. [Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://aclanthology.org/2026.eacl-long.333/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Towards Effective and Efficient Continual Pre-training of Large Language Models](https://aclanthology.org/2025.acl-long.289/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://aclanthology.org/2021.acl-long.234/) *— 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)*
7. [Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation](https://aclanthology.org/P19-1123/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
8. [From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?](https://aclanthology.org/2020.acl-main.660/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
9. [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://aclanthology.org/2023.acl-long.606/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction](https://aclanthology.org/2021.naacl-main.444/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
11. [History for Visual Dialog: Do we really need it?](https://aclanthology.org/2020.acl-main.728/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
12. [Message Passing for Hyper-Relational Knowledge Graphs](https://arxiv.org/abs/2009.10847) *— 2020 · arXiv:2009.10847*
13. [Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play](https://arxiv.org/abs/1703.05407) *— 2017 · arXiv:1703.05407*
14. [Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction](https://aclanthology.org/D13-1204/) *— 2013 · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing*
15. [Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence](https://arxiv.org/abs/1906.01965) *— 2019 · arXiv:1906.01965*
16. [Adversarial Generation of Natural Language](https://aclanthology.org/W17-2629/) *— 2017 · Proceedings of the 2nd Workshop on Representation Learning for NLP*
17. [Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning](https://aclanthology.org/P16-1013/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models](https://arxiv.org/abs/2108.06084) *— 2021 · arXiv:2108.06084*
19. [Generalizing Question Answering System with Pre-trained Language Model Fine-tuning](https://aclanthology.org/D19-5827/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
20. [Teacher-Student Curriculum Learning](https://arxiv.org/abs/1707.00183) *— 2017 · arXiv:1707.00183*
21. [Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning](https://arxiv.org/abs/2506.06632) *— 2025 · arXiv:2506.06632*
22. [MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering](https://aclanthology.org/2023.acl-long.348/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Pre-training a BERT with Curriculum Learning by Increasing Block-Size of Input Text](https://aclanthology.org/2021.ranlp-1.112/) *— 2021 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)*
24. [Self-Paced Learning for Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.80/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
25. [Extractive Summarization Using Multi-Task Learning with Document Classification](https://aclanthology.org/D17-1223/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
26. [A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation](https://aclanthology.org/2022.acl-long.550/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://aclanthology.org/2022.acl-long.524/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Learning Algebraic Recombination for Compositional Generalization](https://aclanthology.org/2021.findings-acl.97/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
29. [The Return of Lexical Dependencies: Neural Lexicalized PCFGs](https://aclanthology.org/2020.tacl-1.42/) *— 2020 · Transactions of the Association for Computational Linguistics*
30. [Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds](https://aclanthology.org/D19-1434/) *— 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)*
31. [CLIMB – Curriculum Learning for Infant-inspired Model Building](https://aclanthology.org/2023.conll-babylm.10/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
32. [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://arxiv.org/abs/2110.00685) *— 2021 · arXiv:2110.00685*
33. [When Do Curricula Work?](https://arxiv.org/abs/2012.03107) *— 2020 · arXiv:2012.03107*
34. [Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability](https://aclanthology.org/P17-1075/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
35. [SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark](https://aclanthology.org/2024.acl-short.11/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
36. [Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning](https://aclanthology.org/2021.naacl-main.434/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
37. [Automatic Curriculum Learning For Deep RL: A Short Survey](https://arxiv.org/abs/2003.04664) *— 2020 · arXiv:2003.04664*
38. [Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation](https://aclanthology.org/P19-1591/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
39. [Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets](https://aclanthology.org/N18-1199/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
40. [Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix](https://aclanthology.org/P17-1040/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Instruction Tuning with Human Curriculum](https://aclanthology.org/2024.findings-naacl.82/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
42. [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://aclanthology.org/2024.lrec-main.1192/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
43. [Modeling Easiness for Training Transformers with Curriculum Learning](https://aclanthology.org/2023.ranlp-1.101/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
44. [Position: AI Evaluation Should Learn from How We Test Humans](https://arxiv.org/abs/2306.10512) *— 2023 · arXiv:2306.10512*
45. [ALLSH: Active Learning Guided by Local Sensitivity and Hardness](https://aclanthology.org/2022.findings-naacl.99/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
46. [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://aclanthology.org/2022.emnlp-main.526/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
47. [Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition](https://aclanthology.org/2022.naacl-main.243/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
48. [Peano: Learning Formal Mathematical Reasoning](https://arxiv.org/abs/2211.15864) *— 2022 · arXiv:2211.15864*
49. [Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking](https://aclanthology.org/2021.acl-short.111/) *— 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)*
50. [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://arxiv.org/abs/2006.08381) *— 2020 · arXiv:2006.08381*
51. [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*
52. [From General LLM to Translation: How We Dramatically Improve Translation Quality Using Human Evaluation Data for LLM Finetuning](https://aclanthology.org/2024.wmt-1.17/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
53. [The Unreasonable Effectiveness of Easy Training Data for Hard Tasks](https://aclanthology.org/2024.acl-long.378/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition](https://aclanthology.org/2022.emnlp-main.236/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
55. [Hierarchical Curriculum Learning for AMR Parsing](https://aclanthology.org/2022.acl-short.37/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
56. [Critical Thinking for Language Models](https://aclanthology.org/2021.iwcs-1.7/) *— 2021 · Proceedings of the 14th International Conference on Computational Semantics (IWCS)*
57. [Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning](https://aclanthology.org/2021.eacl-main.61/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
58. [Learning to Teach](https://arxiv.org/abs/1805.03643) *— 2018 · arXiv:1805.03643*
59. [A Roadmap towards Machine Intelligence](https://arxiv.org/abs/1511.08130) *— 2015 · arXiv:1511.08130*
60. [TIP: Token Importance in On-Policy Distillation](https://arxiv.org/abs/2604.14084) *— 2026 · arXiv:2604.14084*
61. [Self-Evolving Curriculum for LLM Reasoning](https://arxiv.org/abs/2505.14970) *— 2025 · arXiv:2505.14970*
62. [Data Selection Curriculum for Neural Machine Translation](https://aclanthology.org/2022.findings-emnlp.113/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
63. [Active Curriculum Learning](https://aclanthology.org/2021.internlp-1.6/) *— 2021 · Proceedings of the First Workshop on Interactive Learning for Natural Language Processing*
64. [Dialogue Response Selection with Hierarchical Curriculum Learning](https://aclanthology.org/2021.acl-long.137/) *— 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)*
65. [Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks](https://aclanthology.org/W17-7509/) *— 2017 · Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)*
66. [CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions](https://aclanthology.org/2024.emnlp-main.561/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
67. [Is Child-Directed Speech Effective Training Data for Language Models?](https://aclanthology.org/2024.emnlp-main.1231/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
68. [Orion-14B: Open-source Multilingual Large Language Models](https://arxiv.org/abs/2401.12246) *— 2024 · arXiv:2401.12246*
69. [Generalization on the Unseen, Logic Reasoning and Degree Curriculum](https://arxiv.org/abs/2301.13105) *— 2023 · arXiv:2301.13105*
70. [On Curriculum Learning for Commonsense Reasoning](https://aclanthology.org/2022.naacl-main.72/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
71. [Progressive Self-Training with Discriminator for Aspect Term Extraction](https://aclanthology.org/2021.emnlp-main.23/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
72. [Decoding Strategies for Neural Referring Expression Generation](https://aclanthology.org/W18-6563/) *— 2018 · Proceedings of the 11th International Conference on Natural Language Generation*
73. [ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning](https://arxiv.org/abs/2501.06590) *— 2025 · arXiv:2501.06590*
74. [Data-driven Discovery with Large Generative Models](https://arxiv.org/abs/2402.13610) *— 2024 · arXiv:2402.13610*
75. [Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum](https://arxiv.org/abs/2405.13226) *— 2024 · arXiv:2405.13226*
76. [LeanAgent: Lifelong Learning for Formal Theorem Proving](https://arxiv.org/abs/2410.06209) *— 2024 · arXiv:2410.06209*
77. [Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages](https://aclanthology.org/2024.loresmt-1.1/) *— 2024 · Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)*
78. [Interpretable Low-Resource Legal Decision Making](https://arxiv.org/abs/2201.01164) *— 2022 · arXiv:2201.01164*
79. [Progressive Multi-Granularity Training for Non-Autoregressive Translation](https://aclanthology.org/2021.findings-acl.247/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
80. [BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps](https://aclanthology.org/2020.acl-main.229/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
81. [Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction](https://aclanthology.org/D19-1037/) *— 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)*
82. [Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study](https://aclanthology.org/D18-1500/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
83. [Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback](https://aclanthology.org/2026.findings-acl.1685/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
84. [Can Large Reasoning Models Self-Train?](https://arxiv.org/abs/2505.21444) *— 2025 · arXiv:2505.21444*
85. [Estimating Machine Translation Difficulty](https://aclanthology.org/2025.findings-emnlp.1317/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
86. [Neuro-Symbolic Data Generation for Math Reasoning](https://arxiv.org/abs/2412.04857) *— 2024 · arXiv:2412.04857*
87. [Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On](https://arxiv.org/abs/2407.08348) *— 2024 · arXiv:2407.08348*
88. [Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation](https://aclanthology.org/2023.findings-emnlp.597/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
89. [TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes](https://arxiv.org/abs/2311.10797) *— 2023 · arXiv:2311.10797*
90. [Understanding Translationese in Cross-Lingual Summarization](https://aclanthology.org/2023.findings-emnlp.250/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
91. [Predicting Difficulty and Discrimination of Natural Language Questions](https://aclanthology.org/2022.acl-short.15/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
92. [Gradual Fine-Tuning for Low-Resource Domain Adaptation](https://aclanthology.org/2021.adaptnlp-1.22/) *— 2021 · Proceedings of the Second Workshop on Domain Adaptation for NLP*
93. [xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering](https://aclanthology.org/2021.acl-long.477/) *— 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)*
94. [Neural Execution of Graph Algorithms](https://arxiv.org/abs/1910.10593) *— 2019 · arXiv:1910.10593*
95. [Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks](https://aclanthology.org/D17-1255/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
96. [Code-Switching Curriculum Learning for Multilingual Transfer in LLMs](https://aclanthology.org/2025.findings-acl.407/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
97. [Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment](https://aclanthology.org/2024.lrec-main.1460/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
98. [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://arxiv.org/abs/2202.02794) *— 2022 · arXiv:2202.02794 · 39th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 2022*
99. [Challenges of Neural Machine Translation for Short Texts](https://aclanthology.org/2022.cl-2.3/) *— 2022 · Computational Linguistics*
100. [Competence-based Curriculum Learning for Multilingual Machine Translation](https://aclanthology.org/2021.findings-emnlp.212/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*

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