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# Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 4

600 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/dua-2019-drop-reading-comprehension-benchmark-requiring/)

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1. [Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills](https://aclanthology.org/2022.acl-long.416/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [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*
3. [Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding](https://aclanthology.org/2022.naacl-main.28/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
4. [Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering](https://aclanthology.org/2022.coling-1.152/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
5. [UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation](https://aclanthology.org/2022.emnlp-main.508/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
6. [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.455/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
7. [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)*
8. [A Dataset for Answering Time-Sensitive Questions](https://arxiv.org/abs/2108.06314) *— 2021 · arXiv:2108.06314*
9. [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://aclanthology.org/2021.naacl-main.365/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
10. [A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering](https://aclanthology.org/2021.acl-long.318/) *— 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)*
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. [Benchmarking Robustness of Machine Reading Comprehension Models](https://aclanthology.org/2021.findings-acl.56/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
13. [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*
14. [Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval](https://aclanthology.org/2021.acl-long.118/) *— 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)*
15. [Competency Problems: On Finding and Removing Artifacts in Language Data](https://aclanthology.org/2021.emnlp-main.135/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
16. [Constrained Language Models Yield Few-Shot Semantic Parsers](https://aclanthology.org/2021.emnlp-main.608/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
17. [Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification](https://aclanthology.org/2021.emnlp-main.651/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
18. [Controllable Open-ended Question Generation with A New Question Type Ontology](https://aclanthology.org/2021.acl-long.502/) *— 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)*
19. [Coreference Reasoning in Machine Reading Comprehension](https://aclanthology.org/2021.acl-long.448/) *— 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)*
20. [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*
21. [Database reasoning over text](https://aclanthology.org/2021.acl-long.241/) *— 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)*
22. [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://aclanthology.org/2021.tacl-1.21/) *— 2021*
23. [Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation](https://aclanthology.org/2021.emnlp-main.756/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
24. [DynaSent: A Dynamic Benchmark for Sentiment Analysis](https://aclanthology.org/2021.acl-long.186/) *— 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)*
25. [Dynabench: Rethinking Benchmarking in NLP](https://aclanthology.org/2021.naacl-main.324/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
26. [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*
27. [Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP](https://aclanthology.org/2021.acl-long.345/) *— 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)*
28. [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*
29. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
30. [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) *— 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. [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://aclanthology.org/2021.emnlp-main.300/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
32. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
33. [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)*
34. [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/abs/2112.06905) *— 2021 · arXiv:2112.06905*
35. [Have You Seen That Number? Investigating Extrapolation in Question Answering Models](https://aclanthology.org/2021.emnlp-main.563/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
36. [How Fast can BERT Learn Simple Natural Language Inference?](https://aclanthology.org/2021.eacl-main.51/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
37. [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://aclanthology.org/2021.emnlp-main.696/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
38. [Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model](https://aclanthology.org/2021.findings-emnlp.265/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
39. [Investigating the Limitations of Transformers with Simple Arithmetic Tasks](https://arxiv.org/abs/2102.13019) *— 2021 · arXiv:2102.13019*
40. [Learning by Fixing: Solving Math Word Problems with Weak Supervision](https://arxiv.org/abs/2012.10582) *— 2021 · AAAI 2021 (arXiv 2012.10582)*
41. [Learning from Limited Labels for Long Legal Dialogue](https://aclanthology.org/2021.nllp-1.20/) *— 2021 · Proceedings of the Natural Legal Language Processing Workshop 2021*
42. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
43. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
44. [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*
45. [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*
46. [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://aclanthology.org/2021.findings-emnlp.350/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
47. [Numerical reasoning in machine reading comprehension tasks: are we there yet?](https://aclanthology.org/2021.emnlp-main.759/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
48. [On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study](https://aclanthology.org/2021.acl-long.517/) *— 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)*
49. [Paired Examples as Indirect Supervision in Latent Decision Models](https://aclanthology.org/2021.emnlp-main.466/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
50. [REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training](https://aclanthology.org/2021.findings-acl.13/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
51. [Reliability Testing for Natural Language Processing Systems](https://aclanthology.org/2021.acl-long.321/) *— 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)*
52. [Representing Numbers in NLP: a Survey and a Vision](https://aclanthology.org/2021.naacl-main.53/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
53. [Robust Question Answering Through Sub-part Alignment](https://aclanthology.org/2021.naacl-main.98/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
54. [SPARQLing Database Queries from Intermediate Question Decompositions](https://aclanthology.org/2021.emnlp-main.708/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
55. [SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval](https://aclanthology.org/2021.naacl-main.47/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
56. [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
57. [Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.113/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
58. [Single-dataset Experts for Multi-dataset Question Answering](https://aclanthology.org/2021.emnlp-main.495/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
59. [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://aclanthology.org/2021.emnlp-main.586/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
60. [Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering](https://aclanthology.org/2021.emnlp-main.562/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
61. [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://aclanthology.org/2021.acl-long.254/) *— 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)*
62. [Team Papelo at FEVEROUS: Multi-hop Evidence Pursuit](https://aclanthology.org/2021.fever-1.5/) *— 2021 · Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)*
63. [Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models](https://aclanthology.org/2021.naacl-main.99/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
64. [The Power of Scale for Parameter-Efficient Prompt Tuning](https://aclanthology.org/2021.emnlp-main.243/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
65. [Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events](https://aclanthology.org/2021.naacl-main.362/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
66. [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)*
67. [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*
68. [What's in Your Head? Emergent Behaviour in Multi-Task Transformer Models](https://aclanthology.org/2021.emnlp-main.646/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
69. [What's in a Name? Answer Equivalence For Open-Domain Question Answering](https://aclanthology.org/2021.emnlp-main.757/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
70. [Why Machine Reading Comprehension Models Learn Shortcuts?](https://aclanthology.org/2021.findings-acl.85/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
71. [A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering](https://arxiv.org/abs/2005.05257) *— 2020 · arXiv:2005.05257*
72. [A Framework for Evaluation of Machine Reading Comprehension Gold Standards](https://aclanthology.org/2020.lrec-1.660/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
73. [A Simple and Effective Model for Answering Multi-span Questions](https://aclanthology.org/2020.emnlp-main.248/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
74. [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.233/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
75. [A guide to the dataset explosion in QA, NLI, and commonsense reasoning](https://md.mova.elamur.ai/free/en/docs/papers/rogers-2020-guide-dataset-explosion-qa-nli/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts*
76. [An Empirical Investigation of Contextualized Number Prediction](https://aclanthology.org/2020.emnlp-main.385/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
77. [Are Pretrained Language Models Symbolic Reasoners over Knowledge?](https://aclanthology.org/2020.conll-1.45/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
78. [Ask to Learn: A Study on Curiosity-driven Question Generation](https://aclanthology.org/2020.coling-main.202/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
79. [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://aclanthology.org/2020.tacl-1.43/) *— 2020*
80. [Benefits of Intermediate Annotations in Reading Comprehension](https://aclanthology.org/2020.acl-main.497/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
81. [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*
82. [Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models](https://aclanthology.org/2020.emnlp-main.557/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
83. [Break It Down: A Question Understanding Benchmark](https://aclanthology.org/2020.tacl-1.13/) *— 2020 · Transactions of the Association for Computational Linguistics*
84. [CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management](https://aclanthology.org/2020.nlpcovid19-2.14/) *— 2020 · Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020*
85. [Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data](https://aclanthology.org/2020.acl-main.463/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
86. [Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures](https://arxiv.org/abs/2007.08970) *— 2020 · arXiv:2007.08970*
87. [DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking](https://aclanthology.org/2020.acl-main.761/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
88. [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*
89. [Dynamic Sampling Strategies for Multi-Task Reading Comprehension](https://aclanthology.org/2020.acl-main.86/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
90. [Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ](https://aclanthology.org/2020.emnlp-demos.17/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
91. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
92. [Exploring and Predicting Transferability across NLP Tasks](https://aclanthology.org/2020.emnlp-main.635/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
93. [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://aclanthology.org/2020.emnlp-main.437/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
94. [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://arxiv.org/abs/2004.07347) *— 2020 · arXiv:2004.07347*
95. [IIRC: A Dataset of Incomplete Information Reading Comprehension Questions](https://aclanthology.org/2020.emnlp-main.86/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
96. [Improving Compositional Generalization in Semantic Parsing](https://aclanthology.org/2020.findings-emnlp.225/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
97. [Improving QA Generalization by Concurrent Modeling of Multiple Biases](https://aclanthology.org/2020.findings-emnlp.74/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
98. [Injecting Numerical Reasoning Skills into Language Models](https://aclanthology.org/2020.acl-main.89/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
99. [Language (Re)modelling: Towards Embodied Language Understanding](https://aclanthology.org/2020.acl-main.559/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
100. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*

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