[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-3/)

[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 «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences» · page 3

# Who cites «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences» · page 3

299 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/khashabi-2018-looking-beyond-surface-challenge-set/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-2/) · **3**

1. [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*
2. [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*
3. [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)*
4. [PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods](https://aclanthology.org/2020.textgraphs-1.11/) *— 2020 · Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)*
5. [Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport](https://aclanthology.org/2020.acl-main.496/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
6. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
7. [Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian](https://aclanthology.org/2020.coling-main.570/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
8. [RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark](https://aclanthology.org/2020.emnlp-main.381/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
9. [STARC: Structured Annotations for Reading Comprehension](https://md.mova.elamur.ai/free/en/docs/papers/berzak-2020-starc-structured-annotations-reading-comprehension/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
10. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
11. [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*
12. [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*
13. [Which *BERT? A Survey Organizing Contextualized Encoders](https://aclanthology.org/2020.emnlp-main.608/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
14. [Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision](https://aclanthology.org/2020.findings-emnlp.97/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
15. [A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC](https://aclanthology.org/N19-1241/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
16. [Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction](https://aclanthology.org/P19-1225/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
17. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
18. [DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension](https://aclanthology.org/Q19-1014/) *— 2019 · Transactions of the Association for Computational Linguistics*
19. [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.mova.elamur.ai/free/en/docs/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) *— our card · 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
20. [Evidence Sentence Extraction for Machine Reading Comprehension](https://aclanthology.org/K19-1065/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
21. [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) *— 2019 · arXiv:1910.10683*
22. [Improving Machine Reading Comprehension with General Reading Strategies](https://aclanthology.org/N19-1270/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
23. [Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text](https://aclanthology.org/P19-1217/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
24. [Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension](https://arxiv.org/abs/1904.09679) *— 2019 · arXiv:1904.09679*
25. [Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs](https://aclanthology.org/P19-1260/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
26. [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473) *— 2019 · arXiv:1910.11473*
27. [Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering](https://aclanthology.org/D19-1260/) *— 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)*
28. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
29. [Repurposing Entailment for Multi-Hop Question Answering Tasks](https://aclanthology.org/N19-1302/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
30. [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://aclanthology.org/P19-1409/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
31. [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://arxiv.org/abs/1905.00537) *— 2019 · arXiv:1905.00537*
32. [Understanding Dataset Design Choices for Multi-hop Reasoning](https://aclanthology.org/N19-1405/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
33. [Using Natural Language Relations between Answer Choices for Machine Comprehension](https://aclanthology.org/N19-1403/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
34. [What''s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering](https://aclanthology.org/D19-1281/) *— 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)*
35. [``Going on a vacation'''' takes longer than ``Going for a walk'''': A Study of Temporal Commonsense Understanding](https://aclanthology.org/D19-1332/) *— 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)*
36. [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://aclanthology.org/D18-1260/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
37. [Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.mova.elamur.ai/free/en/docs/papers/labutov-2018-multi-relational-question-answering-narratives/) *— our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
39. antoine-2024-linguistically-motivated-evaluation-methodology-unraveling
40. belanec-2026-peft-bench-parameter-efficient-fine
41. belanec-2026-peft-factory-unified-parameter-efficient
42. belenki-2025-optimizing-pre-training-data-mixtures
43. bhardwaj-2023-knn-cm-non-parametric-inference
44. carlsson-2021-gandalf-general-character-name-description
45. choudhury-2022-can-edge-probing-tests-reveal
46. du-2023-make-your-decision-convincing-unified
47. du-2024-care-clue-guided-assistant-csrs
48. fei-2022-coarse-fine-hierarchical-multi-task
49. feng-2023-learning-predict-task-transferability-soft
50. gozzi-2025-bidirectional-emotional-influence-human-llm
51. groeneveld-2023-catwalk-unified-language-model-evaluation
52. ha-2023-two-examples-are-better-than
53. hao-2026-bizcompass-benchmarking-reasoning-capabilities-llms
54. hsia-2024-goodhart-s-law-applies-nlp
55. huang-2023-learning-easily-updated-general-purpose
56. javadi-2025-can-stories-help-llms-reason
57. ju-2021-enhancing-multiple-choice-machine-reading
58. jung-2024-is-prompt-transfer-always-effective
59. khanmohammadi-2025-calibrating-llm-confidence-probing-perturbed
60. kim-2025-first-attentions-last-better-exploiting
61. l-2024-hire-high-recall-approximate-top
62. lee-2023-bayesian-multi-task-transfer-learning
63. li-2024-lorasc-expressive-generalizable-low-rank
64. lin-2024-accept-adaptive-codebook-composite-efficient
65. liu-2021-unsupervised-multiple-choices-question-answering
66. liu-2024-hift-hierarchical-full-parameter-fine
67. liu-2024-regmix-data-mixture-regression-language
68. liu-2024-sparse-mezo-less-parameters-better
69. liu-2024-universal-truthfulness-hyperplane-inside-llms
70. liu-2026-chunkft-byte-streamed-optimization-memory
71. mascarell-2024-which-information-matters-dissecting-human
72. mi-2025-kerzoo-kernel-function-informed-zeroth
73. negheimish-2021-discrete-reasoning-templates-natural-language
74. nishida-2024-initialization-large-language-models-reparameterization
75. pang-2023-sharpt-shared-latent-space-prompt
76. peng-2025-muzo-leveraging-multiple-queries-momentum
77. prabhakar-2024-kraken-inherently-parallel-transformers-efficient
78. qin-2025-beyond-output-matching-bidirectional-alignment
79. singh-2023-entity-tracking-effective-use-multi
80. stevens-2021-investigation-language-model-interpretability-sentence
81. sun-2025-loct-instruct-automatic-pipeline-constructing
82. sun-2026-zocheck-cpu-shadow-checkpointing-zeroth
83. tian-2021-shatter-efficient-transformer-encoder-single
84. tu-2024-multi-mask-prefix-tuning-applying
85. wang-2020-overview-sustainlp-2020-shared-task
86. wang-2024-information-theoretic-approach-analyze-nlp
87. wang-2025-mdpo-customized-direct-preference-optimization
88. wynter-2020-optimal-subarchitecture-extraction-bert
89. xiao-2025-scop-evaluating-comprehension-process-large
90. xu-2025-unlocking-effectiveness-lora-fp-seamless
91. yang-2025-position-really-matters-holistic-approach
92. yu-2022-automatic-label-sequence-generation-prompting
93. yu-2025-ssmlora-enhancing-low-rank-adaptation
94. zeng-2024-bikt-enabling-bidirectional-knowledge-transfer
95. zeng-2025-mept-mixture-expert-prompt-tuning
96. zhang-2023-learning-ignore-adversarial-attacks
97. zhang-2025-dynamic-task-vector-grouping-efficient
98. zhang-2025-learning-select-context-demonstration-preferred
99. zhao-2022-lite-unified-modeling-discriminative-reading

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-2/) · **3**

- [← previous Who cites «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences» · page 2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-2/)
- [next → Who cites «Grammatical Framework (GF) for MT in Sublanguage Domains»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khegai-2006-grammatical-framework-gf-mt-sublanguage/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)