---
title: "Who cites Abductive Commonsense Reasoning — page 3"
description: "The papers of the Elamur Research library that cite «Abductive Commonsense Reasoning», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/3/"
---
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# Who cites «Abductive Commonsense Reasoning» · page 3

274 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/bhagavatula-2019-abductive-commonsense-reasoning/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/2/) · **3**

1. [Stop Measuring Calibration When Humans Disagree](https://aclanthology.org/2022.emnlp-main.124/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
2. [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*
3. [Things not Written in Text: Exploring Spatial Commonsense from Visual Signals](https://aclanthology.org/2022.acl-long.168/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data](https://aclanthology.org/2022.acl-long.226/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Transferring Confluent Knowledge to Argument Mining](https://aclanthology.org/2022.coling-1.597/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
6. [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*
7. [UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131) *— 2022 · arXiv:2205.05131*
8. [WordTies: Measuring Word Associations in Language Models via Constrained Sampling](https://aclanthology.org/2022.findings-emnlp.440/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
9. [e-CARE: a New Dataset for Exploring Explainable Causal Reasoning](https://aclanthology.org/2022.acl-long.33/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models](https://arxiv.org/abs/2203.13112) *— 2022 · arXiv:2203.13112*
11. [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*
12. [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*
13. [COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion](https://aclanthology.org/2021.acl-long.395/) *— 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)*
14. [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*
15. [Can Machines Learn Morality? The Delphi Experiment](https://arxiv.org/abs/2110.07574) *— 2021 · arXiv:2110.07574*
16. [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)*
17. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
18. [Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards](https://aclanthology.org/2021.iwcs-1.8/) *— 2021 · Proceedings of the 14th International Conference on Computational Semantics (IWCS)*
19. [Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings](https://aclanthology.org/2021.blackboxnlp-1.3/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
20. [Embracing Ambiguity: Shifting the Training Target of NLI Models](https://aclanthology.org/2021.acl-short.109/) *— 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)*
21. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
22. [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*
23. [Explanations for CommonsenseQA: New Dataset and Models](https://aclanthology.org/2021.acl-long.238/) *— 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)*
24. [Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models](https://aclanthology.org/2021.emnlp-main.445/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
25. [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*
26. [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*
27. [Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models](https://aclanthology.org/2021.emnlp-main.504/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
28. [Improving Abstractive Summarization with Commonsense Knowledge](https://aclanthology.org/2021.ranlp-srw.19/) *— 2021 · Proceedings of the Student Research Workshop Associated with RANLP 2021*
29. [Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference](https://aclanthology.org/2021.findings-emnlp.420/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
30. [Learning Event Graph Knowledge for Abductive Reasoning](https://aclanthology.org/2021.acl-long.403/) *— 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)*
31. [Learning with Different Amounts of Annotation: From Zero to Many Labels](https://aclanthology.org/2021.emnlp-main.601/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
32. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
33. [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://aclanthology.org/2021.naacl-main.138/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
34. [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://aclanthology.org/2021.emnlp-main.54/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
35. [OTTers: One-turn Topic Transitions for Open-Domain Dialogue](https://aclanthology.org/2021.acl-long.194/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
36. [On-the-Fly Attention Modulation for Neural Generation](https://aclanthology.org/2021.findings-acl.107/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
37. [Probing Across Time: What Does RoBERTa Know and When?](https://aclanthology.org/2021.findings-emnlp.71/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
38. [ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://aclanthology.org/2021.findings-acl.317/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
39. [Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models](https://aclanthology.org/2021.acl-long.114/) *— 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)*
40. [RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge](https://aclanthology.org/2021.findings-acl.131/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
41. [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)*
42. [TellMeWhy: A Dataset for Answering Why-Questions in Narratives](https://md.research.elamur.ai/free/en/papers/lal-2021-tellmewhy-dataset-answering-why-questions/) *— our card · 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
43. [Temporal Reasoning on Implicit Events from Distant Supervision](https://aclanthology.org/2021.naacl-main.107/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
44. [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://aclanthology.org/2021.findings-emnlp.422/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
45. [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://arxiv.org/abs/2103.13009) *— 2021 · arXiv:2103.13009*
46. [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*
47. [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*
48. [“I”m Not Mad”: Commonsense Implications of Negation and Contradiction](https://aclanthology.org/2021.naacl-main.346/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
49. [Adversarial Filters of Dataset Biases](https://arxiv.org/abs/2002.04108) *— 2020 · arXiv:2002.04108*
50. [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://aclanthology.org/2020.acl-main.441/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
51. [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
52. [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)*
53. [CSKG: The CommonSense Knowledge Graph](https://arxiv.org/abs/2012.11490) *— 2020 · arXiv:2012.11490 (ESWC 2021 Resource Track)*
54. [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*
55. [Continual Lifelong Learning in Natural Language Processing: A Survey](https://aclanthology.org/2020.coling-main.574/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
56. [Do We Need to Create Big Datasets to Learn a Task?](https://aclanthology.org/2020.sustainlp-1.23/) *— 2020 · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing*
57. [INFOTABS: Inference on Tables as Semi-structured Data](https://aclanthology.org/2020.acl-main.210/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
58. [INSET: Sentence Infilling with INter-SEntential Transformer](https://aclanthology.org/2020.acl-main.226/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
59. [Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph](https://aclanthology.org/2020.emnlp-main.54/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
60. [Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs](https://aclanthology.org/2020.findings-emnlp.253/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
61. [New Protocols and Negative Results for Textual Entailment Data Collection](https://aclanthology.org/2020.emnlp-main.658/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
62. [Paragraph-level Commonsense Transformers with Recurrent Memory](https://arxiv.org/abs/2010.01486) *— 2020 · arXiv:2010.01486*
63. [ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning](https://aclanthology.org/2020.emnlp-main.85/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
64. [SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations](https://aclanthology.org/2020.acl-main.502/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
65. [Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering](https://aclanthology.org/2020.emnlp-main.11/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
66. [Social Commonsense Reasoning with Multi-Head Knowledge Attention](https://aclanthology.org/2020.findings-emnlp.267/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
67. [Thinking Like a Skeptic: Defeasible Inference in Natural Language](https://aclanthology.org/2020.findings-emnlp.418/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
68. [Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty](https://aclanthology.org/2020.nlpcss-1.13/) *— 2020 · Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science*
69. [Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning](https://aclanthology.org/2020.emnlp-main.61/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
70. [VisualCOMET: Reasoning about the Dynamic Context of a Still Image](https://arxiv.org/abs/2004.10796) *— 2020 · arXiv:2004.10796*
71. [Visuo-Linguistic Question Answering (VLQA) Challenge](https://aclanthology.org/2020.findings-emnlp.413/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
72. [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://aclanthology.org/2020.emnlp-main.734/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
73. [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://aclanthology.org/2020.emnlp-main.185/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
74. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*

Pages: [1](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/2/) · **3**