--- title: "Who cites Gated-Attention Readers for Text Comprehension" description: "107 papers of the Elamur Research library cite «Gated-Attention Readers for Text Comprehension», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/dhingra-2016-gated-attention-readers-text-comprehension/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/dhingra-2016-gated-attention-readers-text-comprehension/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Gated-Attention Readers for Text Comprehension](https://md.research.elamur.ai/free/en/papers/dhingra-2016-gated-attention-readers-text-comprehension/) / cited by # Who cites «Gated-Attention Readers for Text Comprehension» 107 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «Gated-Attention Readers for Text Comprehension»](https://md.research.elamur.ai/free/en/papers/dhingra-2016-gated-attention-readers-text-comprehension/) 1. [**Bidirectional Attention Flow for Machine Comprehension arXiv:1611.01603**Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al.*2016 · 1,988 citations*](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/) 2. [**RACE: Large-scale ReAding Comprehension Dataset From Examinations**Guokun Lai, Qizhe Xie, Hanxiao Liu, et al.*2017 · 1,638 citations*](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/) 3. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/) 4. **[Crowdsourcing Multiple Choice Science Questions](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/) [Workshop on Noisy User-generated Text](https://md.research.elamur.ai/free/en/venues/wnut/)**Johannes Welbl, Nelson F. Liu, Matt Gardner*2017 · 524 citations* 5. **[LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2020-logiqa-challenge-dataset-machine-reading/) [International Joint Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/international-joint-conference-on-artificial-intelligence/)**Jian Liu, Leyang Cui, Hanmeng Liu, et al.*2020 · 404 citations* 6. [**How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks**Divyansh Kaushik, Zachary C. Lipton*2018 · 364 citations*](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/) 7. **[DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension](https://md.research.elamur.ai/free/en/papers/sun-2019-dream-challenge-data-set-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Kai Sun, Dian Yu, Jianshu Chen, et al.*2019 · 334 citations* 8. **[Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge](https://md.research.elamur.ai/free/en/papers/mihaylov-2018-knowledgeable-reader-enhancing-cloze-style/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Todor Mihaylov, Anette Frank*2018 · 263 citations* 9. **[Commonsense for Generative Multi-Hop Question Answering Tasks](https://md.research.elamur.ai/free/en/papers/bauer-2018-commonsense-generative-multi-hop-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lisa Bauer, Yicheng Wang, Mohit Bansal*2018 · 229 citations* 10. **[Tracking the World State with Recurrent Entity Networks](https://md.research.elamur.ai/free/en/papers/henaff-2016-tracking-world-state-recurrent-entity/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Mikael Henaff, Jason Weston, Arthur Szlam, et al.*2016 · 228 citations* 11. **[Denoising Distantly Supervised Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/lin-2018-denoising-distantly-supervised-open-domain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yankai Lin, Haozhe Ji, Zhiyuan Liu, et al.*2018 · 199 citations* 12. **[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* 13. **[Who did What: A Large-Scale Person-Centered Cloze Dataset](https://md.research.elamur.ai/free/en/papers/onishi-2016-who-did-what-large-scale/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Takeshi Onishi, Hai Wang, Mohit Bansal, et al.*2016 · 176 citations* 14. **[Improving Machine Reading Comprehension with General Reading Strategies](https://md.research.elamur.ai/free/en/papers/sun-2019-improving-machine-reading-comprehension-general/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kai Sun, Dian Yu, Dong Yu, et al.*2019 · 172 citations* 15. [**A Span-Extraction Dataset for Chinese Machine Reading Comprehension arXiv:1810.07366 · EMNLP 2019 5886-5891**Yiming Cui, Ting Liu, Wanxiang Che, et al.*2018 · 160 citations*](https://md.research.elamur.ai/free/en/papers/cui-2018-span-extraction-dataset-chinese/) 16. **[Neural Models for Reasoning over Multiple Mentions Using Coreference](https://md.research.elamur.ai/free/en/papers/dhingra-2018-neural-models-reasoning-over-multiple/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bhuwan Dhingra, Qiao Jin, Zhilin Yang, et al.*2018 · 151 citations* 17. [**Dialogue Act Recognition via CRF-Attentive Structured Network arXiv:1711.05568**Zheqian Chen, Rongqin Yang, Zhou Zhao, et al.*2017 · 133 citations*](https://md.research.elamur.ai/free/en/papers/chen-2017-dialogue-act-recognition-crf-attentive/) 18. **[ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://md.research.elamur.ai/free/en/papers/zheng-2019-chid-large-scale-chinese/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chujie Zheng, Minlie Huang, Aixin Sun*2019 · 119 citations* 19. [**Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds**Igor Labutov, Bishan Yang, Anusha Prakash, et al.*2018 · 17 citations*](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/) 88 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/dhingra-2016-gated-attention-readers-text-comprehension/) — Gated-Attention Readers for Text Comprehension