“Cloze Procedure”: A New Tool for Measuring Readability

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 48,293 · see citing works

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works

Working Memory

Baddeley, A. D., & Hitch, G. (1974) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60452-1 · Cited by 9,860 · see citing works

Electrophysiology reveals semantic memory use in language comprehension.

Kutas, M., & Federmeier, K. D. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01560-6 · Cited by 2,124 · see citing works

Unified Language Model Pre-training for Natural Language Understanding and Generation

Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., & Hon, H.-W. (2019) · arXiv · doi:10.48550/arxiv.1905.03197 · Cited by 1,225 · see citing works

Pre-trained models: Past, present and future

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works

Health Literacy Among Medicare Enrollees in a Managed Care Organization

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Age of onset and nativelikeness in a second language: listener perception versus linguistic scrutiny.

Abrahamsson, N., & Hyltenstam, K. (2009) · Language Learning · doi:10.1111/j.1467-9922.2009.00507.x · Cited by 815 · see citing works

Latent retrieval for weakly supervised open domain question answering

Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771 · see citing works

The effect of word predictability on reading time is logarithmic

Smith, N. J., & Levy, R. (2013) · Cognition · doi:10.1016/j.cognition.2013.02.013 · Cited by 725 · see citing works

End-to-End Human Pose and Mesh Reconstruction with Transformers

Lin, K., Wang, L., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00199 · Cited by 740 · see citing works

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

Mostafazadeh, N., Chambers, N., He, X., Parikh, D., Batra, D., Vanderwende, L., Kohli, P., & Allen, J. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1098 · Cited by 598 · see citing works

Shared computational principles for language processing in humans and deep language models

Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01026-4 · Cited by 447 · see citing works

A review of large language models and autonomous agents in chemistry

Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274 · see citing works

The Psychology of Reading for Pleasure: Needs and Gratifications

Nell, V. (1988) · Reading Research Quarterly · doi:10.2307/747903 · Cited by 187 · see citing works

Determinants of individual differences and gender differences in knowledge

Ackerman, P. L., Bowen, K. R., Beier, M. E., & Kanfer, R. (2001) · Journal of Educational Psychology · doi:10.1037/0022-0663.93.4.797 · Cited by 189 · see citing works

Self-supervised learning for videos: A survey

Schiappa, M. C., Rawat, Y. S., & Shah, M. (2023) · ACM Computing Surveys · doi:10.1145/3577925 · Cited by 155 · see citing works

A Practical Survey on Faster and Lighter Transformers

Fournier, Q., Caron, G. M., & Aloise, D. (2023) · ACM Computing Surveys · doi:10.1145/3586074 · Cited by 145 · see citing works

Functional neuroanatomy of meaning acquisition from context

Mestres-Missé, A., Càmara, E., Rodriguez-Fornells, A., Rotte, M., & Münte, T. F. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20150 · Cited by 129 · see citing works

Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Dziri, N., Madotto, A., Zaïane, O., & Bose, A. J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.168 · Cited by 81 · see citing works

TIMEDIAL: Temporal Commonsense Reasoning in Dialog

Qin, L., Gupta, A., Upadhyay, S., He, L., Choi, Y., & Faruqui, M. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.549 · Cited by 59 · see citing works

Is time up for the Flesch measure of reading ease?

Hartley, J. (2016) · Scientometrics · doi:10.1007/s11192-016-1920-7 · Cited by 35 · see citing works

Towards employing native information in citation function classification

Zhang, Y., Zhao, R., Wang, Y., Chen, H., Mahmood, A., Zaib, M., Zhang, W. E., & Sheng, Q. Z. (2022) · Scientometrics · doi:10.1007/s11192-021-04242-0 · Cited by 28 · see citing works

Self-supervised Learning for DNA sequences with circular dilated convolutional networks

Cheng, L., Yu, T., Khalitov, R., & Yang, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.12.002 · Cited by 12 · see citing works

How do listeners time response articulation when answering questions? The role of speech rate.

Corps, R. E., Gambi, C., & Pickering, M. J. (2020) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/xlm0000759 · Cited by 10 · see citing works

Automatic Generation of Multiple-choice Cloze-test Questions for Lao Language Learning

Qiu, X., Xue, H., Liang, L., Xie, Z., Liao, S., & Shi, G. (2021) · 2021 International Conference on Asian Language Processing (IALP) · doi:10.1109/ialp54817.2021.9675153 · Cited by 7 · see citing works

SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories

Bogin, B., Yang, K., Gupta, S., Richardson, K., Bransom, E., Clark, P., Sabharwal, A., & Khot, T. (2024) · arXiv · doi:10.48550/arxiv.2409.07440

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