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# Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning» · page 2

266 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/huang-2019-cosmos-qa-machine-reading-comprehension/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/huang-2019-cosmos-qa-machine-reading-comprehension/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/huang-2019-cosmos-qa-machine-reading-comprehension-3/)

1. [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://aclanthology.org/2022.emnlp-main.812/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
2. [Analytical Reasoning of Text](https://aclanthology.org/2022.findings-naacl.177/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
3. [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://aclanthology.org/2022.gem-1.5/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
4. [Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension](https://aclanthology.org/2022.findings-acl.82/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
5. [Attention-Focused Adversarial Training for Robust Temporal Reasoning](https://aclanthology.org/2022.lrec-1.800/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
6. [CoSe-Co: Text Conditioned Generative CommonSense Contextualizer](https://aclanthology.org/2022.naacl-main.83/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
7. [Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey](https://arxiv.org/abs/2201.12438) *— 2022 · arXiv:2201.12438*
8. [ConTinTin: Continual Learning from Task Instructions](https://aclanthology.org/2022.acl-long.218/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://aclanthology.org/2022.acl-long.244/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding](https://aclanthology.org/2022.naacl-main.234/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
11. [DiscoSense: Commonsense Reasoning with Discourse Connectives](https://aclanthology.org/2022.emnlp-main.703/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
12. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
13. [EvEntS ReaLM: Event Reasoning of Entity States via Language Models](https://aclanthology.org/2022.emnlp-main.129/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
14. [Improving In-Context Few-Shot Learning via Self-Supervised Training](https://aclanthology.org/2022.naacl-main.260/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
15. [Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization](https://aclanthology.org/2022.emnlp-main.105/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
16. [Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning](https://aclanthology.org/2022.acl-long.5/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [Leveraging Large Language Models for Multiple Choice Question Answering](https://arxiv.org/abs/2210.12353) *— 2022 · arXiv:2210.12353*
18. [Leveraging QA Datasets to Improve Generative Data Augmentation](https://aclanthology.org/2022.emnlp-main.660/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
19. [Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text](https://aclanthology.org/2022.findings-acl.127/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
20. [Logical Reasoning for Task Oriented Dialogue Systems](https://aclanthology.org/2022.ecnlp-1.10/) *— 2022 · Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*
21. [Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension](https://aclanthology.org/2022.coling-1.126/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
22. [Narrative Why-Question Answering: A Review of Challenges and Datasets](https://aclanthology.org/2022.gem-1.48/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
23. [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*
24. [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://aclanthology.org/2022.coling-1.319/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
25. [Probing Script Knowledge from Pre-Trained Models](https://aclanthology.org/2022.umios-1.10/) *— 2022 · Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)*
26. [QuALITY: Question Answering with Long Input Texts, Yes!](https://aclanthology.org/2022.naacl-main.391/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
27. [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://aclanthology.org/2022.findings-acl.168/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
28. [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://arxiv.org/abs/2212.07919) *— 2022 · arXiv:2212.07919*
29. [Reframing Instructional Prompts to GPTk's Language](https://aclanthology.org/2022.findings-acl.50/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
30. [Retrieval Augmentation for Commonsense Reasoning: A Unified Approach](https://aclanthology.org/2022.emnlp-main.294/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
31. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [Task Compass: Scaling Multi-task Pre-training with Task Prefix](https://aclanthology.org/2022.findings-emnlp.416/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
33. [The Legal Argument Reasoning Task in Civil Procedure](https://aclanthology.org/2022.nllp-1.17/) *— 2022 · Proceedings of the Natural Legal Language Processing Workshop 2022*
34. [Toward Building a Language Model for Understanding Temporal Commonsense](https://aclanthology.org/2022.aacl-srw.3/) *— 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: Student Research Workshop*
35. [Transferring Confluent Knowledge to Argument Mining](https://aclanthology.org/2022.coling-1.597/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
36. [Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering](https://aclanthology.org/2022.emnlp-main.691/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
37. [UKP-SQUARE: An Online Platform for Question Answering Research](https://aclanthology.org/2022.acl-demo.2/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
38. [UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131) *— 2022 · arXiv:2205.05131*
39. [Using Commonsense Knowledge to Answer Why-Questions](https://aclanthology.org/2022.emnlp-main.79/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
40. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
41. [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://arxiv.org/abs/2208.07998) *— 2022 · arXiv:2208.07998*
42. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
44. [Zero-shot Event Causality Identification with Question Answering](https://aclanthology.org/2022.clib-1.13/) *— 2022 · Proceedings of the Fifth International Conference on Computational Linguistics in Bulgaria (CLIB 2022)*
45. [`Am I the Bad One''? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models](https://aclanthology.org/2022.lrec-1.28/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
46. [ALICE++: Adversarial Training for Robust and Effective Temporal Reasoning](https://aclanthology.org/2021.paclic-1.40/) *— 2021 · Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation*
47. [Automatic Story Generation: Challenges and Attempts](https://aclanthology.org/2021.nuse-1.8/) *— 2021 · Proceedings of the Third Workshop on Narrative Understanding*
48. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
49. [Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.162/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
50. [COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences](https://aclanthology.org/2021.findings-acl.78/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
51. [Can Machines Learn Morality? The Delphi Experiment](https://arxiv.org/abs/2110.07574) *— 2021 · arXiv:2110.07574*
52. [Commonsense-Focused Dialogues for Response Generation: An Empirical Study](https://aclanthology.org/2021.sigdial-1.13/) *— 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
53. [Comparing Test Sets with Item Response Theory](https://aclanthology.org/2021.acl-long.92/) *— 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. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
55. [DAGN: Discourse-Aware Graph Network for Logical Reasoning](https://aclanthology.org/2021.naacl-main.467/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
56. [Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI](https://aclanthology.org/2021.emnlp-main.303/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
57. [ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations](https://aclanthology.org/2021.emnlp-main.597/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
58. [English Machine Reading Comprehension Datasets: A Survey](https://md.mova.elamur.ai/free/en/docs/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/) *— our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
59. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
60. [EventPlus: A Temporal Event Understanding Pipeline](https://aclanthology.org/2021.naacl-demos.7/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations*
61. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
62. [ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.609/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
63. [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://arxiv.org/abs/2104.09570) *— 2021 · arXiv:2104.09570*
64. [Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers](https://aclanthology.org/2021.blackboxnlp-1.42/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
65. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
66. [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://aclanthology.org/2021.acl-long.357/) *— 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)*
67. [Generating Hypothetical Events for Abductive Inference](https://aclanthology.org/2021.starsem-1.6/) *— 2021 · Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics*
68. [Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks](https://aclanthology.org/2021.eacl-main.192/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
69. [Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization](https://aclanthology.org/2021.naacl-main.104/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
70. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
71. [Modeling Context in Answer Sentence Selection Systems on a Latency Budget](https://aclanthology.org/2021.eacl-main.261/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
72. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
73. [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://aclanthology.org/2021.emnlp-main.468/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
74. [NLQuAD: A Non-Factoid Long Question Answering Data Set](https://aclanthology.org/2021.eacl-main.106/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
75. [On Commonsense Cues in BERT for Solving Commonsense Tasks](https://aclanthology.org/2021.findings-acl.61/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
76. [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) *— 2021 · arXiv:2107.13586*
77. [RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms](https://aclanthology.org/2021.emnlp-main.598/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
78. [Syntax-Enhanced Pre-trained Model](https://aclanthology.org/2021.acl-long.420/) *— 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)*
79. [TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models](https://aclanthology.org/2021.acl-long.469/) *— 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. [Towards a Language Model for Temporal Commonsense Reasoning](https://aclanthology.org/2021.ranlp-srw.12/) *— 2021 · Proceedings of the Student Research Workshop Associated with RANLP 2021*
81. [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://arxiv.org/abs/2103.13009) *— 2021 · arXiv:2103.13009*
82. [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://aclanthology.org/2021.acl-long.98/) *— 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)*
83. [What to Pre-Train on? Efficient Intermediate Task Selection](https://aclanthology.org/2021.emnlp-main.827/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
84. [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
85. [A Benchmark Arabic Dataset for Commonsense Explanation](https://arxiv.org/abs/2012.10251) *— 2020 · arXiv:2012.10251*
86. [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://arxiv.org/abs/2005.00247) *— 2020 · arXiv:2005.00247 · Proceedings of EACL 2021*
87. [Adversarial Training for Commonsense Inference](https://aclanthology.org/2020.repl4nlp-1.8/) *— 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP*
88. [Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning](https://aclanthology.org/2020.emnlp-main.58/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
89. [Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models](https://arxiv.org/abs/2005.14709) *— 2020 · arXiv:2005.14709*
90. [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://aclanthology.org/2020.findings-emnlp.165/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
91. [Commonsense Evidence Generation and Injection in Reading Comprehension](https://aclanthology.org/2020.sigdial-1.9/) *— 2020 · Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
92. [Commonsense Reasoning for Natural Language Processing](https://aclanthology.org/2020.acl-tutorials.7/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts*
93. [Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering](https://aclanthology.org/2020.findings-emnlp.369/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
94. [Conversational Machine Comprehension: a Literature Review](https://aclanthology.org/2020.coling-main.247/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
95. [Designing Precise and Robust Dialogue Response Evaluators](https://aclanthology.org/2020.acl-main.4/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
96. [English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://aclanthology.org/2020.aacl-main.56/) *— 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*
97. [Improving Event Detection via Open-domain Trigger Knowledge](https://aclanthology.org/2020.acl-main.522/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
98. [Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks](https://aclanthology.org/2020.deelio-1.9/) *— 2020 · Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures*
99. [Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://aclanthology.org/2020.acl-main.467/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
100. [Is this sentence valid? An Arabic Dataset for Commonsense Validation](https://arxiv.org/abs/2008.10873) *— 2020 · arXiv:2008.10873*

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