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# ❓ Question answering

192 papers · page 3 of 3 · 126–192 · 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.

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126. [**QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines**Valentina Pyatkin, Ayal Klein, Reut Tsarfaty, et al.*2020 · 77 citations*](https://md.research.elamur.ai/free/en/papers/pyatkin-2020-qadiscourse-discourse-relations-as-qa/)
127. [**Bridging Anaphora Resolution as Question Answering**Yufang Hou*2020 · 76 citations*](https://md.research.elamur.ai/free/en/papers/hou-2020-bridging-anaphora-resolution-as-question/)
128. [**Importance of Pronominal Anaphora Resolution in Question Answering Systems**José L. Vicedo, Antonio Ferrández*2000 · 74 citations*](https://md.research.elamur.ai/free/en/papers/vicedo-2000-importance-pronominal-anaphora-resolution-question/)
129. [**CREPE: Open-Domain Question Answering with False Presuppositions**Xinyan Yu, Sewon Min, Luke Zettlemoyer, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/)
130. [**Inquisitive Question Generation for High Level Text Comprehension**Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, et al.*2020 · 65 citations*](https://md.research.elamur.ai/free/en/papers/ko-2020-inquisitive-question-generation-high-level/)
131. [**Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering**Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, et al.*2021 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-which-linguist-invented-lightbulb-presupposition/)
132. [**English Machine Reading Comprehension Datasets: A Survey**Daria Dzendzik, Jennifer Foster, Carl Vogel*2021 · 53 citations*](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
133. [**ArabicaQA: A Comprehensive Dataset for Arabic Question Answering**Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/abdallah-2024-arabicaqa-comprehensive-dataset-arabic-question/)
134. [**STARC: Structured Annotations for Reading Comprehension**Yevgeni Berzak, Jonathan Malmaud, Roger Levy*2020 · 38 citations*](https://md.research.elamur.ai/free/en/papers/berzak-2020-starc-structured-annotations-reading-comprehension/)
135. [**Using Coreference for Question Answering**Thomas S. Morton*1999 · 35 citations*](https://md.research.elamur.ai/free/en/papers/morton-1999-coreference-question-answering/)
136. [**Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension**Shaoru Guo, Yong Guan, Ru Li, et al.*2020 · 30 citations*](https://md.research.elamur.ai/free/en/papers/guo-2020-incorporating-syntax-frame-semantics-neural/)
137. [**IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions**Wenhao Yu, Meng Jiang, Peter Clark, et al.*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/)
138. [**Ellipsis Resolution as Question Answering: An Evaluation**Rahul Aralikatte, Matthew Lamm, Daniel Hardt, et al.*2021 · 19 citations*](https://md.research.elamur.ai/free/en/papers/aralikatte-2021-ellipsis-resolution-as-question-answering/)
139. [**Robustifying Multi-hop QA through Pseudo-Evidentiality Training**Kyungjae Lee, Seung-won Hwang, Sang-eun Han, et al.*2021 · 19 citations*](https://md.research.elamur.ai/free/en/papers/lee-2021-robustifying-multi-hop-qa-through/)
140. [**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/)
141. [**AraMed: Arabic Medical Question Answering using Pretrained Transformer Language Models**Ashwag Alasmari, Sarah Alhumoud, Waad Alshammari*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/alasmari-2024-aramed-arabic-medical-question-answering/)
142. [**Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering**Runxuan Liu, Bei Luo, Jiaqi Li, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-ontology-guided-reverse-thinking-makes/)
143. [**Answer Extraction for Why Arabic Questions Answering Systems: EWAQ**Fatima T. AL-Khawaldeh*2015 · 15 citations*](https://md.research.elamur.ai/free/en/papers/al-khawaldeh-2019-answer-extraction-why-arabic-questions/)
144. [**HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark**Amir DN Cohen, Hilla Merhav, Yoav Goldberg, et al.*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/cohen-2023-heq-large-diverse-hebrew-reading/)
145. [**ParaShoot: A Hebrew Question Answering Dataset**Omri Keren, Omer Levy*2021 · 12 citations*](https://md.research.elamur.ai/free/en/papers/keren-2021-parashoot-hebrew-question-answering-dataset/)
146. [**MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answering**Wen-wai Yim, Asma Ben Abacha, Zixuan Yu, et al.*2026 · 11 citations*](https://md.research.elamur.ai/free/en/papers/yim-2026-morqa-benchmarking-evaluation-metrics-medical/)
147. [**Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering**Neha Srikanth, Rupak Sarkar, Heran Mane, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/srikanth-2024-pregnant-questions-importance-pragmatic-awareness/)
148. [**Coreference Reasoning in Machine Reading Comprehension**Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, et al.*2021 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-coreference-reasoning-machine-reading-comprehension/)
149. [**AmQA: Amharic Question Answering Dataset**Tilahun Abedissa, Ricardo Usbeck, Yaregal Assabie*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/abedissa-2023-amqa-amharic-question-answering-dataset/)
150. [**Optimized Quran Passage Retrieval Using an Expanded QA Dataset and Fine-Tuned Language Models**Mohamed Basem; Islam Oshallah; Baraa Hikal; Ali Hamdi;, Ammar Mohamed*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/basem-2024-optimized-quran-passage-retrieval-using/)
151. [**PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations**Peng Qi, Nina Du, Christopher Manning, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-pragmaticqa-dataset-pragmatic-question-answering/)
152. [**MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages**Dan Saattrup Smart*2026 · 6 citations*](https://md.research.elamur.ai/free/en/papers/smart-2026-multiwikiqa-reading-comprehension-benchmark-300/)
153. [**OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA**Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/alam-2025-oasis-multilingual-multimodal-dataset-culturally/)
154. [**MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark**Mouath Abu-Daoud, Leen Kharouf, Omar El Hajj, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/abu-daoud-2026-medarabench-large-scale-arabic-medical/)
155. [**MizanQA: Benchmarking Large Language Models on Moroccan Legal Question Answering**Adil Bahaj, Mounir Ghogho*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/bahaj-2025-mizanqa-benchmarking-large-language-models/)
156. [**PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering**Wookje Han, Jinsol Park, Kyungjae Lee*2023 · 4 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-prewome-exploiting-presuppositions-as-working/)
157. [**A guide to the dataset explosion in QA, NLI, and commonsense reasoning**Anna Rogers, Anna Rumshisky*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/)
158. [**Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants**Hunzalah Hassan Bhatti, Firoj Alam*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/bhatti-2026-beyond-mcq-open-ended-arabic/)
159. [**HalluScore: Large Language Model Hallucination Question Answering Benchmark**Aisha Alansari, Hamzah Luqman*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/alansari-2026-halluscore-large-language-model-hallucination/)
160. [**Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs**Mohamed Basem, Islam Oshallah, Ali Hamdi, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/basem-2025-few-shot-prompting-extractive-quranic/)
161. [**HistoriQA-ThirdRepublic: Multi-Hop Question Answering Corpus for Historical Research, Parliamentary Debates from the French Third Republic (1870-1940)**Aurelien Pellet, Marie Anna Puren, Julien PEREZ*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/pellet-2026-historiqa-thirdrepublic-multi-hop-question/)
162. [**AmaSQuAD: A Benchmark for Amharic Extractive Question Answering**Nebiyou Daniel Hailemariam, Blessed Guda, Tsegazeab Tefferi*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/hailemariam-2025-amasquad-benchmark-amharic-extractive-question/)
163. [**Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering**Polina Tsvilodub, Robert D. Hawkins, Michael Franke*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tsvilodub-2025-integrating-neural-symbolic-components-model/)
164. [**Multi-Agent Interactive Question Generation Framework for Long Document Understanding**Kesen Wang, Daulet Toibazar, Abdulrahman Alfulayt, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/wang-2025-multi-agent-interactive-question-generation/)
165. [**Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques**Mohammad Tami; Huthaifa I. Ashqar;, Mohammed Elhenawy*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tami-2024-automated-question-generation-science-tests/)
166. [**Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language**Mohammad Sammoudi; Ahmad Habaybeh; Huthaifa I. Ashqar;, Mohammed Elhenawy*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/sammoudi-2024-question-answering-qa-model-personalized/)
167. [**QA-KGNet: Language Model-driven Knowledge Graph Question-answering Model (QA-KGNet: 一种语言模型驱动的知识图谱问答模型)**Shaojie Qiao (乔少杰), Guoping Yang (杨国平), Yong Yu (于泳), et al.*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/qiao-2023-qa-kgnet-language/)
168. [**Harnessing Multilingual Resources to Question Answering in Arabic**Khalid Alnajjar, Mika Hamalainen*2022 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alnajjar-2022-harnessing-multilingual-resources-question-answering/)
169. [**A Binary Problem in Binary QA: Diverse LLMs or Diverse Question Interpretations? That Is the Ensembling Question**Rafael Rosales, Santiago Miret*2026*](https://md.research.elamur.ai/free/en/papers/rosales-2026-binary-problem-binary-qa-diverse/)
170. [**AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications**Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-accuraterag-framework-building-accurate-retrieval/)
171. [**AmharicStoryQA: A Multicultural Story Question Answering Benchmark in Amharic**Israel Abebe Azime, Abenezer Kebede Angamo, Hana Mekonen Tamiru, et al.*2026*](https://md.research.elamur.ai/free/en/papers/azime-2026-amharicstoryqa-multicultural-story-question-answering/)
172. [**Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA**Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/maheshwary-2026-augmenting-llm-reasoning-dynamic-notes/)
173. [**Can Video LLMs See Through Illusions? Video-Illusion QA Benchmark Dataset**Souto Ohira, Tosho Hirasawa, Mamoru Komachi*2026*](https://md.research.elamur.ai/free/en/papers/ohira-2026-can-video-llms-see-through/)
174. [**CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps**Jungmin Yun, June Hyoung Kwon, Youngbin Kim*2026*](https://md.research.elamur.ai/free/en/papers/yun-2026-crit-qa-evaluating-multi-hop/)
175. [**EDRAC: Benchmarking Arabic Dialect Reading Comprehension**Noor Abo Mokh, Kirill Chirkunov, Teresa Lynn, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mokh-2026-edrac-benchmarking-arabic-dialect-reading/)
176. [**EduBench: A Portuguese Benchmark for Open-Ended Discursive Question Answering**Pedro Henrique Paiola, Luís Gabriel Damiati Mendes, Bruno de Oliveira Monchelato, et al.*2026*](https://md.research.elamur.ai/free/en/papers/paiola-2026-edubench-portuguese-benchmark-open-ended/)
177. [**Facilitating Large Language Model-based Multiple-choice Reading Comprehension with Question-solving Thought (基于解题思维机制的大语言模型多项选择阅读理解增强框架)**Yisong Su (苏艺淞), Yuanzhe Zhang (张元哲), Xiangwen Liao (廖祥文), et al.*2026*](https://md.research.elamur.ai/free/en/papers/su-2026-facilitating-large-language/)
178. [**High-Order Question Generation in a Multilingual Educational Context**Suna Seyma Uçar, Itziar Aldabe, Nora Aranberri, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ucar-2026-high-order-question-generation-multilingual/)
179. [**HistoryBankQA: Multilingual Temporal Question Answering on Historical Events**Biswadip Mandal, Anant Khandelwal, Manish Gupta*2026*](https://md.research.elamur.ai/free/en/papers/mandal-2026-historybankqa-multilingual-temporal-question-answering/)
180. [**Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA**Renhao Pei, Siyao Peng, Verena Blaschke, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pei-2026-information-asymmetry-across-language-varieties/)
181. [**Leveraging Decoupled Composition in Large Language Models for Knowledge Base Question Answering (基于解耦与组合的大模型知识问答方法)**Shengze Shi (施圣泽), Li Qin (秦利), Jun Hu (胡军), et al.*2026*](https://md.research.elamur.ai/free/en/papers/shi-2026-leveraging-decoupled-composition/)
182. [**RBR: RAG-Based Open-Domain Question Answering Using a Ranking Approach to Document Retrieval**Priyatam Sai Naravajhula, Vincent Ng*2026*](https://md.research.elamur.ai/free/en/papers/naravajhula-2026-rbr-rag-based-open-domain/)
183. [**Reasoning Graph-Structured Question Answering: Datasets and Insights from LLM Benchmarking**Khin Yone, Devasha Trivedi, Anish Pahilajani, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yone-2026-reasoning-graph-structured-question-answering/)
184. [**ThinkStruct: RST-Aware Attention for Logical Reasoning in Machine Reading Comprehension**Nhi Thao Tran, Tien Le, Toan Pham, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tran-2026-thinkstruct-rst-aware-attention-logical/)
185. [**A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation**Kesen Wang, Daulet Toibazar, Pedro J. Moreno*2025*](https://md.research.elamur.ai/free/en/papers/wang-2025-sea3l-qa-fully-automated-self/)
186. [**Huaputong: Large Language Model for Genealogical Question-answering with Knowledge Reasoning (华谱通: 基于知识推理的家谱问答大语言模型)**Xindong Wu (吴信东), Xingrui Zhuo (卓兴锐), Yongpan Chang (常永泮), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wu-2025-huaputong-large-language/)
187. [**Positive and Negative Chain-of-Thoughts Based Question Answering Method for Table-Text Financial Data (基于正负例思维链的表格-文本混合金融数据自动问答方法)**Xi Li (李希), Xiping Liu (刘喜平), Qing Shu (舒晴), et al.*2025*](https://md.research.elamur.ai/free/en/papers/li-2025-positive-negative-chain/)
188. [**Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)**Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.*2024*](https://md.research.elamur.ai/free/en/papers/antoine-2024-etude-des-facteurs-de-complexite/)
189. [**Research Progress of Video Question Answering Technologies (视频问答技术研究进展)**Cuizhu Bao (包翠竹), Kai Ding (丁凯), Jianfeng Dong (董建锋), et al.*2024*](https://md.research.elamur.ai/free/en/papers/bao-2024-video-question-answering/)
190. [**Researches on Question Answering Over Tables: A Survey (表格问答研究综述)**Hongyi Zhang (张洪廙), Ren Li (李韧), Jianxi Yang (杨建喜), et al.*2024*](https://md.research.elamur.ai/free/en/papers/zhang-2024-researches-question-answering/)
191. [**Survey of Multi-document Machine Reading Comprehension Based on Deep Learning (基于深度学习的多文档机器阅读理解综述)**Feng Gao (高峰), Jiancheng Ni (倪建成), Peng Gao (高鹏), et al.*2023*](https://md.research.elamur.ai/free/en/papers/gao-2023-multi-document-machine/)
192. [**Survey on Commonsense Question Answering (常识问答研究综述)**Yifan Fan (范怡帆), Bowei Zou (邹博伟), Qingting Xu (徐庆婷), et al.*2023*](https://md.research.elamur.ai/free/en/papers/fan-2023-commonsense-question-answering/)

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