WikiQA: A Challenge Dataset for Open-Domain Question Answering

Distributed Representations of Words and Phrases and their Compositionality

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Distributed Representations of Sentences and DocumentsPreprint

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Learning question classifiers

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What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA

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Deep Learning for Answer Sentence SelectionPreprint

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Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions

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Question Answering Using Enhanced Lexical Semantic Models

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Answer Extraction as Sequence Tagging with Tree Edit Distance

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Automatic Feature Engineering for Answer Selection and Extraction

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SQuAD: 100,000+ Questions for Machine Comprehension of Text

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Know What You Don’t Know: Unanswerable Questions for SQuAD

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Natural Questions: A benchmark for question answering research

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Deep Learning--based Text Classification

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A Survey of the Usages of Deep Learning for Natural Language Processing

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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

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Parameter-efficient fine-tuning of large-scale pre-trained language models

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ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

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Bilateral Multi-Perspective Matching for Natural Language Sentences

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A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT

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A Survey on Text Classification: From Traditional to Deep Learning

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SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · arXiv · doi:10.48550/arxiv.1606.05250 · Cited by 678 · see citing works

Gated Self-Matching Networks for Reading Comprehension and Question Answering

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Multitask Prompted Training Enables Zero-Shot Task Generalization

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The Web as a Knowledge-Base for Answering Complex Questions

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Neural Variational Inference for Text ProcessingPreprint

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End-to-End Open-Domain Question Answering with BERTserini

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QAngaroo (MedHop + WikiHop) - Constructing Datasets for Multi-hop Reading Comprehension Across Documents

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T y D i QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages

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MLQA: Evaluating Cross-lingual Extractive Question Answering

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Evolution of Semantic Similarity—A Survey

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A Deep Look into neural ranking models for information retrieval

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Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences

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SuperAgent: A Customer Service Chatbot for E-commerce Websites

Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (2017) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/p17-4017 · Cited by 357 · see citing works

Attentive Pooling NetworksPreprint

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R3: Reinforced Ranker-Reader for Open-Domain Question Answering

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A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

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Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)

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Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering

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Survey on evaluation methods for dialogue systems

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Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

Matsubara, Y., Levorato, M., & Restuccia, F. (2022) · ACM Computing Surveys · doi:10.1145/3527155 · Cited by 327 · see citing works

Question Answering on Freebase via Relation Extraction and Textual Evidence

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Progress in Neural NLP: Modeling, Learning, and Reasoning

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A Diagram is Worth a Dozen Images

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Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

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Question Generation for Question Answering

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Crowdsourcing Multiple Choice Science Questions

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Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement

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A Compare-Aggregate Model for Matching Text SequencesPreprint

Wang, S., & Jiang, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01747 · Cited by 239 · see citing works

Deep Reinforcement Learning for Sequence-to-Sequence Models

Keneshloo, Y., Shi, T., Ramakrishnan, N., & Reddy, C. K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929141 · Cited by 230 · see citing works

How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

Kaushik, D., & Lipton, Z. C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1546 · Cited by 229 · see citing works

KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA

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Learning to Paraphrase for Question Answering

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Inner Attention based Recurrent Neural Networks for Answer Selection

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TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

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Sentence Similarity Learning by Lexical Decomposition and CompositionPreprint

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Simple and Effective Text Matching with Richer Alignment Features

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Muppet: Massive Multi-task Representations with Pre-Finetuning

Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.468 · Cited by 183 · see citing works

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Lv, S., Guo, D., Xu, J., Tang, D., Duan, N., Gong, M., Shou, L., Jiang, D., Cao, G., & Hu, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6364 · Cited by 183 · see citing works

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

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QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Rogers, A., Gardner, M., & Augenstein, I. (2022) · ACM Computing Surveys · doi:10.1145/3560260 · Cited by 177 · see citing works

Coarse-to-Fine Question Answering for Long Documents

Choi, E., Hewlett, D., Uszkoreit, J., Polosukhin, I., Lacoste, A., & Berant, J. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1020 · Cited by 170 · see citing works

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169 · see citing works

Datasets for large language models: a comprehensive survey

Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11403-7 · Cited by 159 · see citing works

Neural Arabic Question Answering

Mozannar, H., Maamary, E., Hajal, K. E., & Hajj, H. M. (2019) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.18653/v1/w19-4612 · Cited by 158 · see citing works

Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks

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Open-Retrieval Conversational Question Answering

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Neural Models for Information RetrievalPreprint

Mitra, B., & Craswell, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.01509 · Cited by 154 · see citing works

Selective Question Answering under Domain Shift

Kamath, A., Jia, R., & Liang, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.503 · Cited by 153 · see citing works

Simple Applications of BERT for Ad Hoc Document RetrievalPreprint

Yang, W., Zhang, H., & Lin, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10972 · Cited by 153 · see citing works

KLUE: Korean Language Understanding EvaluationPreprint

Park, S., Moon, J., Kim, S., Cho, W. I., Han, J., Park, J., Song, C., Kim, J., Song, Y., Oh, T., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.09680 · Cited by 153 · see citing works

JEC-QA: A Legal-Domain Question Answering Dataset

Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6519 · Cited by 152 · see citing works

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

Seo, M., Lee, J., Kwiatkowski, T., Parikh, A. P., Farhadi, A., & Hajishirzi, H. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1436 · Cited by 147 · see citing works

Efficient and Robust Question Answering from Minimal Context over Documents

Min, S., Zhong, V., Socher, R., & Xiong, C. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1160 · Cited by 147 · see citing works

Semi-Supervised QA with Generative Domain-Adaptive Nets

Yang, Z., Hu, J., Salakhutdinov, R., & Cohen, W. W. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1096 · Cited by 142 · see citing works

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.365 · Cited by 138 · see citing works

Poisoning Web-Scale Training Datasets is Practical

Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F. (2024) · 2024 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp54263.2024.00179 · Cited by 138 · see citing works

Poisoning Web-Scale Training Datasets is Practical

Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F. (2023) · arXiv · doi:10.48550/arxiv.2302.10149 · Cited by 138 · see citing works

Conversational question answering: a survey

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Look Before You Leap: An Exploratory Study of Uncertainty Analysis for Large Language Models

Huang, Y., Song, J., Wang, Z., Zhao, S., Chen, H., Juefei-Xu, F., & Ma, L. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3519464 · Cited by 132 · see citing works

Question Answering through Transfer Learning from Large Fine-grained Supervision Data

Min, S., Seo, M., & Hajishirzi, H. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2081 · Cited by 128 · see citing works

WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

Hewlett, D., Lacoste, A., Jones, L., Polosukhin, I., Fandrianto, A., Han, J. J., Kelcey, M., & Berthelot, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1145 · Cited by 125 · see citing works

Who did What: A Large-Scale Person-Centered Cloze Dataset

Onishi, T., Wang, H., Bansal, M., Gimpel, K., & McAllester, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1241 · Cited by 122 · see citing works

A survey on question answering systems over linked data and documents

Dimitrakis, E., Sgontzos, K., & Tzitzikas, Y. (2019) · Journal of Intelligent Information Systems · doi:10.1007/s10844-019-00584-7 · Cited by 122 · see citing works

CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP

Ye, Q., Lin, B. Y., & Ren, X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.572 · Cited by 120 · see citing works

Learning Recurrent Span Representations for Extractive Question AnsweringPreprint

Lee, K., Salant, S., Kwiatkowski, T., Parikh, A. P., Das, D., & Berant, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01436 · Cited by 118 · see citing works

DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents

Zhao, Y. L., Duan, N., Bao, J., Chen, P., Zhou, M., Li, Z., & Zhou, J. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1049 · Cited by 117 · see citing works

Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer Learning

Zhao, C., & He, Y. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313578 · Cited by 116 · see citing works

Multiway Attention Networks for Modeling Sentence Pairs

Tan, C., Wei, F., Wang, W., Lv, W., & Zhou, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/613 · Cited by 114 · see citing works

Transformer-Based Neural Network for Answer Selection in Question Answering

Shao, T., Guo, Y., Chen, H., & Hao, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2900753 · Cited by 114 · see citing works

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question AnsweringPreprint

Lan, W., & Xu, W. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04330 · Cited by 113 · see citing works

Rethinking search: making domain experts out of dilettantes

Metzler, D., Tay, Y., Bahri, D., & Najork, M. (2021) · ACM SIGIR Forum · doi:10.1145/3476415.3476428 · Cited by 112 · see citing works

Decoding Queries: An In-Depth Survey of Quality Techniques for Question Analysis in Arabic Question Answering Systems

Essam, M., Deif, M. A., Attar, H. H., Alrosan, A., Kanan, M. A., & Elgohary, R. (2024) · IEEE Access · doi:10.1109/access.2024.3458466 · Cited by 112 · see citing works

A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets

Zeng, C., Li, S., Li, Q., Hu, J., & Hu, J. (2020) · Applied Sciences · doi:10.3390/app10217640 · Cited by 109 · see citing works

Inter-Weighted Alignment Network for Sentence Pair Modeling

Shen, G., Yang, Y., & Deng, Z. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1122 · Cited by 107 · see citing works

User Intent Prediction in Information-seeking Conversations

Qu, C., Yang, L., Croft, W. B., Zhang, Y., Trippas, J. R., & Qiu, M. (2019) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3295750.3298924 · Cited by 107 · see citing works

Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering

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Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

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