---
title: "Who cites Efficient and Robust Question — 51 citing papers"
description: "51 papers of the Elamur Research library cite «Efficient and Robust Question Answering from Minimal Context over Documents», found in their…"
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# Who cites «Efficient and Robust Question Answering from Minimal Context over Documents»

51 papers of our library cite it (of the 141,840 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «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/)

1. **[Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*
2. [**Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering arXiv:1911.10470**Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, et al.*2019 · 272 citations*](https://md.research.elamur.ai/free/en/papers/asai-2019-learning-retrieve-reasoning-paths-over/)
3. **[Hierarchical Graph Network for Multi-hop Question Answering](https://md.research.elamur.ai/free/en/papers/fang-2020-hierarchical-graph-network-multi-hop/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuwei Fang, Siqi Sun, Zhe Gan, et al.*2020 · 241 citations*
4. **[Dynamically Fused Graph Network for Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/qiu-2019-dynamically-fused-graph-network-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lin Qiu, Yunxuan Xiao, Yanru Qu, et al.*2019 · 237 citations*
5. [**How Context Affects Language Models' Factual Predictions arXiv:2005.04611**Fabio Petroni, Patrick Lewis, Aleksandra Piktus, et al.*2020 · 231 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-context-affects-language-models-factual/)
6. **[Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index](https://md.research.elamur.ai/free/en/papers/seo-2019-real-time-open-domain-question/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, et al.*2019 · 216 citations*
7. **[Unsupervised Question Answering by Cloze Translation](https://md.research.elamur.ai/free/en/papers/lewis-2019-unsupervised-question-answering-cloze-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Patrick Lewis, Ludovic Denoyer, Sebastian Riedel*2019 · 192 citations*
8. **[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 · 178 citations*
9. **[Look at the First Sentence: Position Bias in Question Answering](https://md.research.elamur.ai/free/en/papers/ko-2020-look-first-sentence-position-bias/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, et al.*2020 · 102 citations*
10. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/min-2018-efficient-robust-question-answering-minimal/) — Efficient and Robust Question Answering from Minimal Context over Documents