[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-4/)

[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 «The NarrativeQA Reading Comprehension Challenge» · page 4

# Who cites «The NarrativeQA Reading Comprehension Challenge» · page 4

435 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/kocisky-2018-narrativeqa-reading-comprehension-challenge/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-3/) · **4** · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-5/)

1. [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*
2. [Commonsense for Generative Multi-Hop Question Answering Tasks](https://aclanthology.org/D18-1454/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
3. [Comparative Analysis of Neural QA models on SQuAD](https://aclanthology.org/W18-2610/) *— 2018 · Proceedings of the Workshop on Machine Reading for Question Answering*
4. [Cut to the Chase: A Context Zoom-in Network for Reading Comprehension](https://aclanthology.org/D18-1054/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
5. [Deep Learning: A Critical Appraisal](https://arxiv.org/abs/1801.00631) *— 2018 · arXiv:1801.00631*
6. [DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension](https://aclanthology.org/P18-1156/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [Efficient and Robust Question Answering from Minimal Context over Documents](https://aclanthology.org/P18-1160/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Learning to Search in Long Documents Using Document Structure](https://aclanthology.org/C18-1014/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
9. [Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension](https://aclanthology.org/D18-1238/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
10. [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)*
11. [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267) *— 2018 · arXiv:1809.08267*
12. [QuAC: Question Answering in Context](https://aclanthology.org/D18-1241/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
13. [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://arxiv.org/abs/1810.12885) *— 2018 · arXiv:1810.12885*
14. [The price of debiasing automatic metrics in natural language evalaution](https://aclanthology.org/P18-1060/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Towards Inference-Oriented Reading Comprehension: ParallelQA](https://aclanthology.org/W18-1001/) *— 2018 · Proceedings of the Workshop on Generalization in the Age of Deep Learning*
16. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
17. [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://arxiv.org/abs/1711.05073) *— 2017 · arXiv:1711.05073*
18. [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://arxiv.org/abs/1611.09268) *— 2016 · arXiv:1611.09268*
19. ahrabian-2024-adaptation-unlimiformer-decoder-only-transformers
20. aksu-2021-velocidapter-task-oriented-dialogue-comprehension
21. alla-2025-budgetmem-training-free-selective-memory
22. an-2025-long-chain-thought-fine-tuning
23. angelidis-2019-book-qa-stories-challenges-opportunities
24. bae-2023-sound-story-multi-modal-storytelling
25. bansal-2023-few-shot-unified-question-answering
26. berger-2021-increasing-sentence-level-comprehension-through
27. berhe-2022-survey-narrative-structure-linguistic-theories
28. bhat-2021-hierarchical-encoders-modeling-interpreting-screenplays
29. bleiweiss-2023-two-step-text-summarization-long
30. bonomo-2025-literaryqa-effective-evaluation-long-document
31. boreshban-2023-robustqa-framework-adversarial-text-generation
32. byerly-2026-self-consistency-falls-short-adverse
33. cao-2025-sync-synthetic-long-context-understanding
34. carlsson-2021-gandalf-general-character-name-description
35. chawla-2025-fb-rag-improving-rag-forward
36. chen-2020-developing-how-tip-machine-comprehension
37. chen-2025-longleader-comprehensive-leaderboard-large-language
38. chen-2026-beyond-chunking-discourse-aware-hierarchical
39. chen-2026-msa-memory-sparse-attention-efficient
40. chernyavskiy-2026-discorag-discourse-aware-agent-query
41. chitale-2025-evaluating-effectiveness-scalability-llm-based
42. datta-2021-virtual-pre-service-teacher-assessment
43. ding-2026-hierarchical-token-prepending-enhancing-information
44. djenovic-2026-development-serbian-qa-datasets-through
45. do-2023-machine-reading-comprehension-vietnamese-customer
46. duan-2026-chunks-as-arms-multi-armed
47. ficek-2024-gpt-vs-retro-exploring-intersection
48. fung-2023-deepmaven-deep-question-answering-long
49. ghosh-2025-onebench-test-them-all-sample
50. gill-2025-what-has-been-lost-synthetic
51. gillick-2018-telling-stories-soundtracks-empirical-analysis
52. gioffre-2026-indaqa2-large-italian-narrative-qa
53. glavas-2026-intra-prompt-parallel-decoding-common
54. grijalba-2026-enhancing-evaluating-tabular-models-fly
55. gu-2026-brief-pro-universal-context-compression
56. gupta-2025-novelhopqa-diagnosing-multi-hop-reasoning
57. gupta-2026-learning-optimal-message-representations-agentic
58. he-2026-budgetdraft-acceptance-aware-multi-view
59. hu-2026-memory-matters-more-event-centric
60. hu-2026-optimizing-native-sparse-attention-latent
61. huang-2025-multi-document-question-answering-scientific
62. jahan-2026-lfqa-hp-1m-large-scale
63. javadi-2025-can-stories-help-llms-reason
64. jin-2026-sara-selective-adaptive-retrieval-augmented
65. johnson-2025-ficsim-dataset-multi-faceted-semantic
66. kalbaliyev-2024-narrative-question-answering-skills
67. krishnamurthy-2021-reference-based-weak-supervision-answer
68. kumar-2022-augmenting-ebooks-recommended-questions-contrastive
69. laitenberger-2025-stronger-baselines-retrieval-augmented-generation
70. leite-2025-advancing-question-generation-joint-narrative
71. li-2023-question-interlocutor-scope-realized-graph
72. li-2025-context-efficient-retrieval-factual-decomposition
73. li-2025-glider-global-local-instruction-driven
74. li-2025-llm-as-judge-failures-automating
75. li-2025-unirag-unified-query-understanding-method
76. lin-2021-readonce-transformers-reusable-representations-text
77. lin-2025-facilitating-long-context-understanding-supervised
78. liu-2024-bridging-context-gaps-leveraging-coreference
79. liu-2024-chatqa-surpassing-gpt-4-conversational
80. liu-2025-hsgm-hierarchical-segment-graph-memory
81. liu-2026-instella-moe-technical-report
82. lizzo-2025-unlearn-efficient-removal-knowledge-large
83. lv-2024-critiprefill-segment-wise-criticality-based
84. madhusudan-2025-fine-tuned-llms-are-time
85. mahalingam-2025-sketch-structured-knowledge-enhanced-text
86. marcuzzo-2025-morables-benchmark-assessing-abstract-moral
87. matsushima-2026-aqpim-breaking-pim-capacity-wall
88. meng-2025-understanding-bottlenecks-efficiently-serving-llm
89. metel-2024-batch-max-higher-llm-throughput
90. moroni-2025-what-we-learned-continually-training
91. nie-2023-attenwalker-unsupervised-long-document-question
92. novak-2023-question-answering-benchmark-database-hungarian
93. park-2025-accelerating-llm-inference-precomputed-query
94. park-2026-tempokv-timely-staging-llm-kv
95. qian-2025-boosting-long-context-information-seeking
96. qiu-2025-think-both-ways-teacher-student
97. s-2024-tale-two-revisions-summarizing-changes
98. safaya-2024-neurocache-efficient-vector-retrieval-long
99. shen-2025-adapting-large-language-models-movie
100. sheng-2025-dynamic-chunking-selection-reading-comprehension

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-3/) · **4** · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-5/)

- [← previous Who cites «The NarrativeQA Reading Comprehension Challenge» · page 3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-3/)
- [next → Who cites «The NarrativeQA Reading Comprehension Challenge» · page 5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kocisky-2018-narrativeqa-reading-comprehension-challenge-5/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)