Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473

Learning deep structured semantic models for web search using clickthrough data

Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,120

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804

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

Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690

Reading Wikipedia to answer open-domain questions

Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546

Learning to Build Reasoning Chains by Reliable Path Retrieval

Zhu, M., Weng, Y., He, S., Wang, C., Liu, K., Cai, L., & Zhao, J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097146 · Cited by 2

An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT

Kang, B., Kim, Y., & Shin, Y. (2023) · Applied Sciences · doi:10.3390/app132413177 · Cited by 2

A BERT-Based Semantic Matching Ranker for Open-Domain Question Answering

Xu, S., Liu, F., Huang, Z., Peng, Y., & Li, D. (2020) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3443279.3443301 · Cited by 3

A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context

Luo, M., Chen, S., & Baral, C. R. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.23 · Cited by 2

Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question AnsweringPreprint

Shao, N., Cui, Y., Liu, T., Wang, S., & Hu, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03741 · Cited by 6

Answering Any-hop Open-domain Questions with Iterative Document RerankingPreprint

Nie, P., Zhang, Y., Ramamurthy, A., & Song, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07465 · Cited by 2

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact VerificationPreprint

Si, J., Zhou, D., Li, T., Shi, X., & He, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01191 · Cited by 2

Knowledge-Enhanced Graph Attention Network for Fact Verification

Chen, C., Zheng, J., & Chen, H. (2021) · Mathematics · doi:10.3390/math9161949 · Cited by 2

A multilingual semantic search chatbot framework

Vinay, R., Thejas, B. U., Sharma, H. A. V., Shobha, G., & Ghuli, P. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp2333-2341 · Cited by 1

Entity-augmented neuroscience knowledge retrieval using ontology and semantic understanding capability of LLM

Ta, P., Venkatesaperumal, S., Ram, K., & Sivaprakasam, M. (2025) · Neuroscience Informatics · doi:10.1016/j.neuri.2025.100237 · Cited by 1

Multi-hop reading comprehension over Wikipedia

Li, D., Zhong, Y., Wei, C., & Han, Y. (2022) · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID) · doi:10.1117/12.2631115 · Cited by 1

mReFinED: An Efficient End-to-End Multilingual Entity Linking System

Limkonchotiwat, P., Cheng, W., Christodoulopoulos, C., Saffari, A., & Lehmann, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1007 · Cited by 1

Open Domain Question Answering Based on Retriever-Reader Architecture

Zheng, D., Yang, J., & Yong, B. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-92632-8_68 · Cited by 1

Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval MarginalizationPreprint

Ni, A., Gardner, M., & Dasigi, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12235 · Cited by 1

Meta Answering for Machine ReadingPreprint

Börschinger, B., Boyd-Graber, J. L., Buck, C., Bulian, J., Ciaramita, M., Huebscher, M. C., Gajewski, W., Kilcher, Y., Nogueira, R. F., & Saralegui, L. S. (2019) · arXiv (Cornell University) · doi:10.3929/ethz-b-000393755 · Cited by 1

ForecastQA: Machine Comprehension of Temporal Text for Answering Forecasting QuestionsPreprint

Jin, W., Kim, S., & Ren, X. (2020) · arXiv (Cornell University)

Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question AnsweringPreprint

Liang, Z., Zhao, Y., & Surdeanu, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10791

Robustifying Multi-hop QA through Pseudo-Evidentiality TrainingPreprint

Lee, K., Hwang, S.-W., Han, S.-E., & Lee, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03242

Accessing Diverse Web Knowledge with Natural Language Interface

chen, W. (2021) · eScholarship (California Digital Library)

A Flexible and Extensible Framework for Multiple Answer Modes Question Answering.

Fan, C.-C., Kuo, C.-C., Luo, S.-B., Liao, P.-J., Chang, K.-Y., Hsu, C.-W., Wu, M.-T., Tsai, S.-H., Wu, T.-M., Smolka, A., et al. (2021) · International Conference on Computational Linguistics

SR3: Sentence Ranking, Reasoning, and Replication for Scenario-Based Essay Question Answering

Chen, Y., Zhao, Y., & Cheng, G. (2022) · IEEE Internet Computing · doi:10.1109/mic.2022.3161587

Design of Large-Scale Electronic Information Intelligent Cache System Based on Knowledge Map

Shouyu, L., Kun, Z., Wei, J., He, Y., Yuanwei, Y., & Li, Y. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1983-3_58

Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)

(2023) · doi:10.18653/v1/2023.fever-1

CNGT: Co-attention Networks with Graph Transformer for Fact Verification

Yuan, J., Chen, C., Hou, C., & Yuan, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46664-9_39

Toward complex data structure aggregation and truth discovery

Sabetpour, N. (2022) · doi:10.31274/td-20240329-813

LogSay: An Efficient Comprehension System for Log Numerical Reasoning

Qi, J., Luan, Z., Huang, S., Fung, C., & Yang, H. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3386068 · Cited by 1

A Tree-Shaped Fuzzy Clustering Answer Retrieval Model Based on Question Alignment

Lang, Q., Witold Pedrycz, C., Liu, X., & Fang, Y. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3431938

Claim polarity analysis from conflicting sources

Senouci, A., Meziane, H., & Benbernou, S. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00634-6

A Rule-Based Multidimensional Axiomatic Fuzzy Set Knowledge Graph Question-Answering Model

Du, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9671-7_3

KQFV: a knowledge-enhanced method using question answering for fact verification

Bian, Y., & Ma, T. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00956-2

Supporting Retriever’s Training by Joint Likelihood-Based Soft-Label Generation

Peng, T., Chen, R., Xie, X. C., Lin, D. X., & Cui, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_23

Rethinking Data Use in Large Language Models

Min, S., Hajishirzi, H., & Zettlemoyer, L. (2025) · Computational Linguistics · doi:10.1162/coli.a.573

A comprehensive survey on table question answering: Datasets, methods and future directions

Xu, W., Liu, Y., Lu, L., Li, H., & Sun, G. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114459

Evidence-chain-driven multimodal retrieval question answering

Yang, S., Wu, A., Wu, X., Zhao, J., Wang, L., & He, L. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116358

Adaptive Hyper-parameter Learning for Deep Semantic Retrieval

Li, M., Yuan, C., Wang, H., Wang, P., Zhuo, J., Wang, B., Liu, L., & Xu, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.72 · Cited by 3

Improving Machine Reading Comprehension with General Reading Strategies

Sun, K., Yu, D., Yu, D., & Cardie, C. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1270 · Cited by 134

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Huang, L., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1243 · Cited by 302

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Qin, L., Galley, M., Brockett, C., Liu, X., Gao, X., Dolan, B., Choi, Y., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1539 · Cited by 100

Attention-aware semantic relevance predicting Chinese sentence reading

Sun, K. (2025) · Universitätsbibliothek Tübingen · doi:10.15496/publikation-110952