--- title: "Who cites Denoising Distantly Supervised — 57 citing papers" description: "57 papers of the Elamur Research library cite «Denoising Distantly Supervised Open-Domain Question Answering», found in their…" canonical: "https://research.elamur.ai/free/en/papers/lin-2018-denoising-distantly-supervised-open-domain/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lin-2018-denoising-distantly-supervised-open-domain/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Denoising Distantly Supervised Open-Domain…](https://md.research.elamur.ai/free/en/papers/lin-2018-denoising-distantly-supervised-open-domain/) / cited by # Who cites «Denoising Distantly Supervised Open-Domain Question Answering» 57 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 «Denoising Distantly Supervised Open-Domain Question Answering»](https://md.research.elamur.ai/free/en/papers/lin-2018-denoising-distantly-supervised-open-domain/) 1. **[Dense Passage Retrieval for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,375 citations* 2. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters arXiv:2002.01808**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 665 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/) 3. **[Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2019-multi-passage-bert-globally-normalized/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhiguo Wang, Patrick Ng, Xiaofei Ma, et al.*2019 · 303 citations* 4. [**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/) 5. **[MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://md.research.elamur.ai/free/en/papers/talmor-2019-multiqa-empirical-investigation-generalization-transfer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Talmor, Jonathan Berant*2019 · 234 citations* 6. [**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/) 7. **[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* 8. **[Neural Arabic Question Answering](https://md.research.elamur.ai/free/en/papers/mozannar-2019-neural-arabic-question-answering/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Hussein Mozannar, Elie Maamary, Karl El Hajal, et al.*2019 · 205 citations* 9. **[Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/chen-2020-open-domain-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Danqi Chen, Wen-tau Yih*2020 · 127 citations* 10. **[Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference !](https://md.research.elamur.ai/free/en/papers/tang-2023-can-diffusion-model-achieve-better/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zecheng Tang, Pinzheng Wang, Keyan Zhou, et al.*2023 · 18 citations* 47 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/lin-2018-denoising-distantly-supervised-open-domain/) — Denoising Distantly Supervised Open-Domain Question Answering