--- title: "Question answering — papers, page 2" description: "Question answering: 183 papers of the Elamur Research library · page 2 of 8, the most cited first." canonical: "https://research.elamur.ai/free/en/topics/question-answering/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/topics/question-answering/page/2/) [home](https://md.research.elamur.ai/free/en/) / [topics](https://md.research.elamur.ai/free/en/topics/) / [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) / page 2 # ❓ Question answering 183 papers · page 2 of 8 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/) Systems that answer questions from a text, a collection or a knowledge base. [← prev](https://md.research.elamur.ai/free/en/topics/question-answering/) [1](https://md.research.elamur.ai/free/en/topics/question-answering/) **2** [3](https://md.research.elamur.ai/free/en/topics/question-answering/page/3/) … [8](https://md.research.elamur.ai/free/en/topics/question-answering/page/8/) [next →](https://md.research.elamur.ai/free/en/topics/question-answering/page/3/) - [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/) — Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al. · 2022 · 744 citations - [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/) — Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, et al. · 2020 · 735 citations - [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://md.research.elamur.ai/free/en/papers/lewis-2019-mlqa-evaluating-cross-lingual-extractive/) — Patrick Lewis, Barlas Oğuz, Ruty Rinott, et al. · 2019 · 735 citations - [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://md.research.elamur.ai/free/en/papers/clark-2020-tydi-qa-benchmark-information-seeking/) — Jonathan H. Clark, Eunsol Choi, Michael Collins, et al. · 2020 · 691 citations - [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/) — Danqi Chen, Jason Bolton, Christopher D. Manning · 2016 · 689 citations - [WebGPT: Browser-assisted question-answering with human feedback](https://md.research.elamur.ai/free/en/papers/nakano-2021-webgpt-browser-assisted-question-answering/) — Reiichiro Nakano, Jacob Hilton, Suchir Balaji, et al. · 2021 · 687 citations - [ELI5: Long Form Question Answering](https://md.research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/) — Angela Fan, Yacine Jernite, Ethan Perez, et al. · 2019 · 682 citations - [Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings](https://md.research.elamur.ai/free/en/papers/saxena-2020-improving-multi-hop-question-answering/) — Apoorv Saxena, Aditay Tripathi, Partha Talukdar · 2020 · 670 citations - [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/) — Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, et al. · 2018 · 669 citations - [TVQA: Localized, Compositional Video Question Answering](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/) — Jie Lei, Licheng Yu, Mohit Bansal, et al. · 2018 · 669 citations - [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/qu-2021-rocketqa-optimized-training-approach-dense/) — Yingqi Qu, Yuchen Ding, Jing Liu, et al. · 2021 · 601 citations - [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/) — Adams Wei Yu, David Dohan, Minh-Thang Luong, et al. · 2018 · 581 citations - [Dynamic Coattention Networks For Question Answering](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/) — Caiming Xiong, Victor Zhong, Richard Socher · 2016 · 578 citations - [Simple and Effective Multi-Paragraph Reading Comprehension](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/) — Christopher Clark, Matt Gardner · 2017 · 556 citations - [Learning to Compose Neural Networks for Question Answering](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/) — Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al. · 2016 · 547 citations - [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/) — Hu Xu, Bing Liu, Lei Shu, et al. · 2019 · 544 citations - [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/) — Di Jin, Eileen Pan, Nassim Oufattole, et al. · 2020 · 540 citations - [End-to-End Open-Domain Question Answering with BERTserini](https://md.research.elamur.ai/free/en/papers/yang-2019-end-end-open-domain-question/) — Wei Yang, Yuqing Xie, Aileen Lin, et al. · 2019 · 539 citations - [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/) — Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al. · 2019 · 536 citations - [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/) — Jason Weston, Antoine Bordes, Sumit Chopra, et al. · 2015 · 529 citations - [The Natural Language Decathlon: Multitask Learning as Question Answering](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/) — Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al. · 2018 · 523 citations - [Using Semantic Roles to Improve Question Answering](https://md.research.elamur.ai/free/en/papers/shen-2007-semantic-roles-improve-question-answering/) — Dan Shen, Mirella Lapata · 2007 · 522 citations - [Towards Expert-Level Medical Question Answering with Large Language Models](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/) — Karan Singhal, Tao Tu, Juraj Gottweis, et al. · 2023 · 503 citations - [What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA](https://md.research.elamur.ai/free/en/papers/wang-2007-what-is-jeopardy-model-quasi/) — Mengqiu Wang, Noah A. Smith, Teruko Mitamura · 2007 · 503 citations - [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/) — Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al. · 2022 · 490 citations [← prev](https://md.research.elamur.ai/free/en/topics/question-answering/) [1](https://md.research.elamur.ai/free/en/topics/question-answering/) **2** [3](https://md.research.elamur.ai/free/en/topics/question-answering/page/3/) … [8](https://md.research.elamur.ai/free/en/topics/question-answering/page/8/) [next →](https://md.research.elamur.ai/free/en/topics/question-answering/page/3/)