---
title: "Who cites Making Neural QA as Simple as — 90 citing papers"
description: "90 papers of the Elamur Research library cite «Making Neural QA as Simple as Possible but not Simpler», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Making Neural QA as Simple as Possible but not…](https://md.research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/) / cited by

# Who cites «Making Neural QA as Simple as Possible but not Simpler»

90 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «Making Neural QA as Simple as Possible but not Simpler»](https://md.research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/)

1. **[Know What You Don't Know: Unanswerable Questions for SQuAD](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,405 citations*
2. [**Adversarial Examples for Evaluating Reading Comprehension Systems**Robin Jia, Percy Liang*2017 · 1,969 citations*](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)
3. [**The NarrativeQA Reading Comprehension Challenge**Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, et al.*2018 · 1,042 citations*](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
4. **[Gated Self-Matching Networks for Reading Comprehension and Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2017-gated-self-matching-networks-reading/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhui Wang, Nan Yang, Furu Wei, et al.*2017 · 833 citations*
5. **[ELI5: Long Form Question Answering](https://md.research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Angela Fan, Yacine Jernite, Ethan Perez, et al.*2019 · 695 citations*
6. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension arXiv:1804.09541**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 597 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
7. **[Simple and Effective Multi-Paragraph Reading Comprehension](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Christopher Clark, Matt Gardner*2017 · 562 citations*
8. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
9. **[Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://md.research.elamur.ai/free/en/papers/welbl-2018-constructing-datasets-multi-hop-reading/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Johannes Welbl, Pontus Stenetorp, Sebastian Riedel*2018 · 290 citations*
10. **[Reinforced Mnemonic Reader for Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/hu-2017-reinforced-mnemonic-reader-machine-reading/) [International Joint Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/international-joint-conference-on-artificial-intelligence/)**Minghao Hu, Yuxing Peng, Zhen Huang, et al.*2017 · 273 citations*
11. **[Interpretation of Natural Language Rules in Conversational Machine Reading](https://md.research.elamur.ai/free/en/papers/saeidi-2018-interpretation-natural-language-rules-conversational/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Marzieh Saeidi, Max Bartolo, Patrick Lewis, et al.*2018 · 230 citations*
12. **[Efficient and Robust Question Answering from Minimal Context over Documents](https://md.research.elamur.ai/free/en/papers/min-2018-efficient-robust-question-answering-minimal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sewon Min, Victor Zhong, Richard Socher, et al.*2018 · 197 citations*
13. **[DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension](https://md.research.elamur.ai/free/en/papers/saha-2018-duorc-complex-language-understanding-paraphrased/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, et al.*2018 · 190 citations*
14. **[What Makes Reading Comprehension Questions Easier?](https://md.research.elamur.ai/free/en/papers/sugawara-2018-what-makes-reading-comprehension-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Saku Sugawara, Kentaro Inui, Satoshi Sekine, et al.*2018 · 179 citations*
15. **[QuALITY: Question Answering with Long Input Texts, Yes!](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 167 citations*
16. **[Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://md.research.elamur.ai/free/en/papers/ravichander-2019-question-answering-privacy-policies-combining/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Abhilasha Ravichander, Alan W Black, Shomir Wilson, et al.*2019 · 163 citations*
17. **[Understanding Dataset Design Choices for Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2019-understanding-dataset-design-choices-multi/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jifan Chen, Greg Durrett*2019 · 148 citations*
18. **[Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/bartolo-2020-beat-ai-investigating-adversarial-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Max Bartolo, Alastair Roberts, Johannes Welbl, et al.*2020 · 141 citations*
19. **[ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning](https://md.research.elamur.ai/free/en/papers/qin-2021-erica-improving-entity-relation-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yujia Qin, Yankai Lin, Ryuichi Takanobu, et al.*2021 · 139 citations*
20. **[Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://md.research.elamur.ai/free/en/papers/cao-2019-question-answering-reasoning-across-documents/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nicola De Cao, Wilker Aziz, Ivan Titov*2019 · 138 citations*
21. **[Cross-Document Event Coreference Resolution on Discourse Structure](https://md.research.elamur.ai/free/en/papers/chen-2023-cross-document-event-coreference-resolution/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinyu Chen, Sheng Xu, Peifeng Li, et al.*2023 · 16 citations*
22. **[Coreference Reasoning in Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/wu-2021-coreference-reasoning-machine-reading-comprehension/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, et al.*2021 · 11 citations*
23. **[MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://md.research.elamur.ai/free/en/papers/smart-2026-multiwikiqa-reading-comprehension-benchmark-300/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Dan Saattrup Smart*2026 · 6 citations*
24. **[A Lightweight Method to Generate Unanswerable Questions in English](https://md.research.elamur.ai/free/en/papers/gautam-2023-lightweight-method-generate-unanswerable-questions/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vagrant Gautam, Miaoran Zhang, Dietrich Klakow*2023 · 6 citations*
25. **[TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2024-tigqa-expert-annotated-question-answering/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hailay Kidu Teklehaymanot, Dren Fazlija, Niloy Ganguly, et al.*2024 · 4 citations*

65 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/) — Making Neural QA as Simple as Possible but not Simpler