[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/huang-2019-cosmos-qa-machine-reading-comprehension/)

[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 «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning»

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

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** · [2](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 Survey of Automatic Prompt Optimization Techniques](https://aclanthology.org/2025.emnlp-main.1681/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
2. [AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs](https://aclanthology.org/2025.emnlp-main.293/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
3. [CausalEval: Towards Better Causal Reasoning in Language Models](https://aclanthology.org/2025.naacl-long.622/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
4. [Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs](https://aclanthology.org/2025.coling-main.703/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
5. [Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese](https://arxiv.org/abs/2502.12932) *— 2025 · arXiv:2502.12932*
6. [DiPT: Enhancing LLM Reasoning through Diversified Perspective-Taking](https://aclanthology.org/2025.findings-naacl.356/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
7. [FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema](https://aclanthology.org/2025.coling-main.731/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
8. [LLMs May Perform MCQA by Selecting the Least Incorrect Option](https://aclanthology.org/2025.coling-main.390/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
9. [NarratEX Dataset: Explaining the Dominant Narratives in News Texts](https://aclanthology.org/2025.findings-emnlp.1112/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
10. [On the Impact of Fine-Tuning on Chain-of-Thought Reasoning](https://aclanthology.org/2025.naacl-long.584/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
11. [Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles](https://aclanthology.org/2025.emnlp-main.1155/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
12. [Towards Quantifying Commonsense Reasoning with Mechanistic Insights](https://aclanthology.org/2025.naacl-long.487/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
13. [UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions](https://aclanthology.org/2025.findings-acl.423/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
14. [Understanding Silent Data Corruption in LLM Training](https://aclanthology.org/2025.acl-long.996/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://aclanthology.org/2024.findings-acl.334/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
16. [Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification](https://aclanthology.org/2024.findings-acl.618/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
17. [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827) *— 2024 · arXiv:2402.07827*
18. [BEnQA: A Question Answering Benchmark for Bengali and English](https://aclanthology.org/2024.findings-acl.68/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
19. [CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean](https://aclanthology.org/2024.lrec-main.296/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
20. [CLOMO: Counterfactual Logical Modification with Large Language Models](https://aclanthology.org/2024.acl-long.593/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Calibration-Tuning: Teaching Large Language Models to Know What They Don''t Know](https://aclanthology.org/2024.uncertainlp-1.1/) *— 2024 · Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)*
22. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
23. [Desiderata For The Context Use Of Question Answering Systems](https://aclanthology.org/2024.eacl-long.47/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://aclanthology.org/2024.emnlp-main.799/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
25. [EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning](https://aclanthology.org/2024.findings-emnlp.125/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
26. [Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process](https://aclanthology.org/2024.emnlp-main.74/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
27. [Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues](https://aclanthology.org/2024.acl-long.90/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Every Answer Matters: Evaluating Commonsense with Probabilistic Measures](https://aclanthology.org/2024.acl-long.29/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://aclanthology.org/2024.lrec-main.708/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
30. [InstructEval: Systematic Evaluation of Instruction Selection Methods](https://aclanthology.org/2024.findings-naacl.270/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
31. [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://aclanthology.org/2024.emnlp-main.148/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
32. [KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge](https://aclanthology.org/2024.findings-acl.666/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
33. [LC4EE: LLMs as Good Corrector for Event Extraction](https://aclanthology.org/2024.findings-acl.715/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
34. [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://arxiv.org/abs/2406.18403) *— 2024 · arXiv:2406.18403*
35. [Large Language Models Must Be Taught to Know What They Don't Know](https://arxiv.org/abs/2406.08391) *— 2024 · arXiv:2406.08391*
36. [MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://aclanthology.org/2024.acl-long.224/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning](https://aclanthology.org/2024.naacl-long.235/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
38. [Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?](https://aclanthology.org/2024.acl-long.304/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [Multi-Task Learning with Adapters for Plausibility Prediction: Bridging the Gap or Falling into the Trenches?](https://aclanthology.org/2024.insights-1.18/) *— 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP*
40. [RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering](https://aclanthology.org/2024.emnlp-main.249/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
41. [SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://aclanthology.org/2024.naacl-long.22/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
42. [SocREval: Large Language Models with the Socratic Method for Reference-free Reasoning Evaluation](https://aclanthology.org/2024.findings-naacl.175/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
43. [Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the US](https://aclanthology.org/2024.emnlp-main.532/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
44. [Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models](https://aclanthology.org/2024.findings-acl.81/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
45. [The Critique of Critique](https://aclanthology.org/2024.findings-acl.538/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
46. [Think Twice: Perspective-Taking Improves Large Language Models'' Theory-of-Mind Capabilities](https://aclanthology.org/2024.acl-long.451/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt](https://aclanthology.org/2024.lrec-main.1481/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
48. [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://aclanthology.org/2024.findings-acl.844/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
49. [tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework](https://aclanthology.org/2024.lrec-main.1361/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
50. [Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning](https://aclanthology.org/2023.emnlp-demo.13/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
51. [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://arxiv.org/abs/2308.00002) *— 2023 · arXiv:2308.00002*
52. [Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering](https://aclanthology.org/2023.findings-eacl.87/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
53. [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://aclanthology.org/2023.findings-emnlp.677/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
54. [Benchmarks for Automated Commonsense Reasoning: A Survey](https://arxiv.org/abs/2302.04752) *— 2023 · arXiv:2302.04752*
55. [Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models](https://aclanthology.org/2023.crac-main.5/) *— 2023 · Proceedings of the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023)*
56. [CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models](https://aclanthology.org/2023.findings-eacl.23/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
57. [CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks](https://aclanthology.org/2023.emnlp-main.607/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
58. [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://arxiv.org/abs/2302.07926) *— 2023 · arXiv:2302.07926*
59. [Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification](https://aclanthology.org/2023.findings-acl.752/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
60. [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030) *— 2023 · arXiv:2306.09030*
61. [Don''t Blame the Annotator: Bias Already Starts in the Annotation Instructions](https://aclanthology.org/2023.eacl-main.130/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
62. [Evaluating and Improving the Coreference Capabilities of Machine Translation Models](https://aclanthology.org/2023.eacl-main.69/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
63. [Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension](https://aclanthology.org/2023.emnlp-main.9/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
64. [Exploring the Benefits of Training Expert Language Models over Instruction Tuning](https://arxiv.org/abs/2302.03202) *— 2023 · arXiv:2302.03202*
65. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
66. [FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning](https://aclanthology.org/2023.acl-long.454/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
67. [Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference](https://aclanthology.org/2023.findings-acl.30/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
68. [Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning](https://aclanthology.org/2023.findings-acl.148/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
69. [Instructive Dialogue Summarization with Query Aggregations](https://aclanthology.org/2023.emnlp-main.474/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
70. [KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding](https://aclanthology.org/2023.findings-acl.743/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
71. [Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU](https://aclanthology.org/2023.emnlp-main.760/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
72. [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://aclanthology.org/2023.acl-long.659/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
73. [Long-Tailed Question Answering in an Open World](https://aclanthology.org/2023.acl-long.351/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
75. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://arxiv.org/abs/2309.15402) *— 2023 · arXiv:2309.15402*
76. [NormBank: A Knowledge Bank of Situational Social Norms](https://aclanthology.org/2023.acl-long.429/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [Parameter-efficient Weight Ensembling Facilitates Task-level Knowledge Transfer](https://aclanthology.org/2023.acl-short.24/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
78. [Pre-Training to Learn in Context](https://aclanthology.org/2023.acl-long.267/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [Prompting with Pseudo-Code Instructions](https://aclanthology.org/2023.emnlp-main.939/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
80. [QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering](https://aclanthology.org/2023.findings-emnlp.1023/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
81. [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://aclanthology.org/2023.findings-acl.428/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
82. [ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://aclanthology.org/2023.emnlp-main.622/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
83. [Shepherd: A Critic for Language Model Generation](https://arxiv.org/abs/2308.04592) *— 2023 · arXiv:2308.04592*
84. [Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs](https://arxiv.org/abs/2311.17371) *— 2023 · arXiv:2311.17371*
85. [Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement](https://aclanthology.org/2023.trustnlp-1.28/) *— 2023 · Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)*
86. [TIES-Merging: Resolving Interference When Merging Models](https://arxiv.org/abs/2306.01708) *— 2023 · arXiv:2306.01708*
87. [TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks](https://arxiv.org/abs/2310.00752) *— 2023 · arXiv:2310.00752*
88. [TaskWeb: Selecting Better Source Tasks for Multi-task NLP](https://aclanthology.org/2023.emnlp-main.680/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
89. [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://arxiv.org/abs/2301.13688) *— 2023 · arXiv:2301.13688*
90. [The Web Can Be Your Oyster for Improving Language Models](https://aclanthology.org/2023.findings-acl.46/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
91. [Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models](https://arxiv.org/abs/2310.00836) *— 2023 · arXiv:2310.00836*
92. [Unified Demonstration Retriever for In-Context Learning](https://aclanthology.org/2023.acl-long.256/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
93. [Using contradictions improves question answering systems](https://aclanthology.org/2023.acl-short.72/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
94. [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://arxiv.org/abs/2312.09390) *— 2023 · arXiv:2312.09390*
95. [Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation](https://arxiv.org/abs/2306.05783) *— 2023 · arXiv:2306.05783*
96. [Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks](https://aclanthology.org/2023.findings-acl.246/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
97. [``World Knowledge'''' in Multiple Choice Reading Comprehension](https://aclanthology.org/2023.fever-1.5/) *— 2023 · Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)*
98. [常识问答研究综述](https://jos.org.cn/jos/article/abstract/6913) *— 2023 · 软件学报 (Journal of Software)*
99. [A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation](https://aclanthology.org/2022.findings-naacl.172/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
100. [A Review on Language Models as Knowledge Bases](https://arxiv.org/abs/2204.06031) *— 2022 · arXiv:2204.06031*

Pages: **1** · [2](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/)

- [← previous Who cites «Structured Perceptron with Inexact Search»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/huang-2012-structured-perceptron-inexact-search/)
- [next → Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning» · page 2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/huang-2019-cosmos-qa-machine-reading-comprehension-2/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)