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

FEVER: a Large-scale Dataset for Fact Extraction and VERification

Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (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/n18-1074 · Cited by 1,343

A Deep Relevance Matching Model for Ad-hoc Retrieval

Guo, J., Fan, Y., Ai, Q., & Croft, W. B. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983769 · Cited by 858

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

Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.1162/tacl_a_00021 · Cited by 474

Learning to Match using Local and Distributed Representations of Text for Web Search

Mitra, B., Díaz, F., & Craswell, N. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052579 · Cited by 465

Simple and Effective Multi-Paragraph Reading Comprehension

Clark, C., & Gardner, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1078 · Cited by 434

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

Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12053 · Cited by 351

Neural Ranking Models with Weak Supervision

Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080832 · Cited by 317

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

Nie, Y., Chen, H., & Bansal, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016859 · Cited by 286

Automated Fact Checking: Task formulations, methods and future directionsPreprint

Thorne, J. P., & Vlachos, A. (2018) · arXiv (Cornell University) · doi:10.17863/cam.78745 · Cited by 260

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1259 · Cited by 222

Natural language inference

Manning, C. D., & MacCartney, B. (2009) · Cited by 209

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification

Hanselowski, A., Zhang, H., Li, Z., Sorokin, D., Schiller, B., Schulz, C., & Gurevych, I. (2018) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/w18-5516 · Cited by 184

UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)

Yoneda, T., Mitchell, J., Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the First Workshop on Fact Extraction and VERification (FEVER) · doi:10.18653/v1/w18-5515 · Cited by 101

Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering

Lee, J., Yun, S., Kim, H., Ko, M., & Kang, J. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1053 · Cited by 97

Training a Ranking Function for Open-Domain Question Answering

Htut, P. M., Bowman, S. R., & Cho, K. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n18-4017 · Cited by 50

Dense passage retrieval for open-domain question answering

Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575

A Survey on Automated Fact-Checking

Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00454 · Cited by 500

Fine-grained Fact Verification with Kernel Graph Attention Network

Liu, Z., Xiong, C., Sun, M., & Liu, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.655 · Cited by 236

Hierarchical Graph Network for Multi-hop Question Answering

Fang, Y., Sun, S., Gan, Z., Pillai, R., Wang, S., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.710 · Cited by 176

Coreferential Reasoning Learning for Language Representation

Ye, D., Lin, Y., Du, J., Liu, Z., Li, P., Sun, M., & Liu, Z. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.582 · Cited by 181

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