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# Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 5

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. [Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification](https://aclanthology.org/2020.coling-main.466/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
2. [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
3. [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://arxiv.org/abs/2007.08124) *— 2020 · arXiv:2007.08124*
4. [Low-Resource Generation of Multi-hop Reasoning Questions](https://aclanthology.org/2020.acl-main.601/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
5. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
6. [Machine Reasoning: Technology, Dilemma and Future](https://aclanthology.org/2020.emnlp-tutorials.1/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
7. [Methods for Numeracy-Preserving Word Embeddings](https://aclanthology.org/2020.emnlp-main.384/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
8. [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://aclanthology.org/2020.acl-main.130/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
9. [Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering](https://aclanthology.org/2020.acl-main.654/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
10. [Obtaining Faithful Interpretations from Compositional Neural Networks](https://aclanthology.org/2020.acl-main.495/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
11. [Overestimation of Syntactic Representation in Neural Language Models](https://aclanthology.org/2020.acl-main.160/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
12. [Question Directed Graph Attention Network for Numerical Reasoning over Text](https://aclanthology.org/2020.emnlp-main.549/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
13. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
14. [Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension](https://aclanthology.org/2020.coling-main.329/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
15. [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://md.mova.elamur.ai/free/en/docs/papers/ning-2020-torque-reading-comprehension-dataset-temporal/) *— our card · 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
16. [TaPas: Weakly Supervised Table Parsing via Pre-training](https://aclanthology.org/2020.acl-main.398/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
17. [Teaching Machine Comprehension with Compositional Explanations](https://aclanthology.org/2020.findings-emnlp.145/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
18. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
19. [ToTTo: A Controlled Table-To-Text Generation Dataset](https://aclanthology.org/2020.emnlp-main.89/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
20. [Training Question Answering Models From Synthetic Data](https://aclanthology.org/2020.emnlp-main.468/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
21. [UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://aclanthology.org/2020.findings-emnlp.171/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
22. [Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering](https://aclanthology.org/2020.acl-main.414/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
23. [What Question Answering can Learn from Trivia Nerds](https://aclanthology.org/2020.acl-main.662/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
24. [What-if I ask you to explain: Explaining the effects of perturbations in procedural text](https://aclanthology.org/2020.findings-emnlp.300/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
25. [A Discrete Hard EM Approach for Weakly Supervised Question Answering](https://aclanthology.org/D19-1284/) *— 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)*
26. [A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning](https://aclanthology.org/D19-1170/) *— 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)*
27. [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](https://aclanthology.org/D19-3002/) *— 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): System Demonstrations*
28. [An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering](https://aclanthology.org/D19-5829/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
29. [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://aclanthology.org/D19-1107/) *— 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)*
30. [Can You Unpack That? Learning to Rewrite Questions-in-Context](https://aclanthology.org/D19-1605/) *— 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. [Comprehensive Multi-Dataset Evaluation of Reading Comprehension](https://aclanthology.org/D19-5820/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
32. [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://aclanthology.org/D19-1534/) *— 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)*
33. [Domain-agnostic Question-Answering with Adversarial Training](https://aclanthology.org/D19-5826/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
34. [EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/) *— our card · 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
35. [Evaluating Question Answering Evaluation](https://aclanthology.org/D19-5817/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
36. [Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension](https://aclanthology.org/D19-1609/) *— 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)*
37. [Let''s Ask Again: Refine Network for Automatic Question Generation](https://aclanthology.org/D19-1326/) *— 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)*
38. [Movie Plot Analysis via Turning Point Identification](https://aclanthology.org/D19-1180/) *— 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)*
39. [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://aclanthology.org/P19-1485/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
40. [NumNet: Machine Reading Comprehension with Numerical Reasoning](https://aclanthology.org/D19-1251/) *— 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)*
41. [On Making Reading Comprehension More Comprehensive](https://aclanthology.org/D19-5815/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
42. [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473) *— 2019 · arXiv:1910.11473*
43. [Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/) *— our card · 2019 · arXiv:1908.05803*
44. [Reasoning Over Paragraph Effects in Situations](https://aclanthology.org/D19-5808/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
45. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
46. abootorabi-2026-almieyar-oryx-bloombench-bilingual-multimodal
47. agrawal-2024-exmy-data-type-technique-arbitrary
48. ahmed-2025-bennumeval-benchmark-assess-llms-numerical
49. ahrabian-2025-systematic-analysis-base-model-choice
50. amer-2026-flop-efficient-training-early-stopping
51. arunkumar-2023-real-time-visual-feedback-guide
52. bai-2025-ramqa-unified-framework-retrieval-augmented
53. balepur-2026-benchmarker-education-inspired-toolkit-highlighting
54. bansal-2023-few-shot-unified-question-answering
55. bojic-2025-smartminer-extracting-evaluating-smart-goals
56. bottman-2026-operadic-consistency-label-free-signal
57. bruyn-2022-20q-overlap-free-world-knowledge
58. bui-2025-vmlu-benchmarks-comprehensive-benchmark-toolkit
59. burg-2025-aligning-black-box-language-models
60. byun-2025-textit-d-gen-automatic-distractor
61. chegini-2026-reasoning-s-razor-reasoning-improves
62. chen-2022-teaching-neural-module-networks-do
63. chen-2024-s3prompt-instructing-model-self-calibration
64. chen-2026-agentslimming-efficient-cost-aware-multi
65. chen-2026-relevance-sufficient-evidence-detecting-evidence
66. chen-2026-rethinking-evaluation-llm-hallucination-detection
67. chen-2026-sempa-improving-sentence-embeddings-large
68. chinnappa-2020-wikipossessions-possession-timeline-generation-as
69. cho-2026-generalizing-numerical-reasoning-table-data
70. chow-2023-travlr-now-you-see-it
71. chu-2024-navigate-through-enigmatic-labyrinth-survey
72. chu-2025-enhancing-numerical-reasoning-vietnamese-financial
73. contalbo-2025-gri-qa-comprehensive-benchmark-table
74. demir-2026-timeres-turkish-benchmark-evaluating-temporal
75. ding-2025-sulora-subspace-low-rank-adaptation
76. dinu-2020-joint-translation-unit-conversion-end
77. dong-2025-longred-mitigating-short-text-degradation
78. du-2026-native-hybrid-attention-efficient-sequence
79. dutta-2026-carve-content-aware-recurrent-value
80. el-2025-inefficiencies-meta-agents-agent-design
81. fang-2025-patch-psychometrics-assisted-benchmarking-large
82. faure-2025-moviecore-cognitive-reasoning-movies
83. feng-2025-dive-into-moe-diversity-enhanced
84. fu-2025-model-merging-knowledge-editing
85. geng-2025-great-memory-shallow-reasoning-limits
86. ghiasvand-2025-decentralized-low-rank-fine-tuning
87. ghosal-2023-retag-reasoning-aware-table-analytic
88. gill-2025-what-has-been-lost-synthetic
89. gonzalez-2024-numdecoders-semeval-2024-task-7
90. grimm-2021-biquad-qa-based-deeper-text
91. groeneveld-2023-catwalk-unified-language-model-evaluation
92. guo-2020-understanding-unnatural-questions-improves-reasoning
93. guo-2021-improving-numerical-reasoning-skills-modular
94. guo-2026-beyond-facts-benchmarking-distributional-reading
95. ha-2025-graph-based-agent-approach-numerical
96. hashimoto-2024-mlt-dr-multi-lingual-task
97. he-2025-esgenius-benchmarking-llms-environmental-social
98. he-2026-knowledge-distillation-during-mid-training
99. ho-2022-how-well-do-multi-hop
100. ho-2026-reassessing-extractive-qa-datasets-scale

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