Building a Large Annotated Corpus of English: The Penn Treebank

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 · see citing works

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Ma, X., & Hovy, E. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1101 · Cited by 2,567 · see citing works

Linguistic complexity: locality of syntactic dependencies

Gibson, E. (1998) · Cognition · doi:10.1016/s0010-0277(98)00034-1 · Cited by 2,537 · see citing works

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967 · see citing works

Domain adaptation with structural correspondence learning

Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543 · see citing works

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834 · see citing works

Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science

Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854 · see citing works

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

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works

Grammar as a Foreign Language

Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2014) · arXiv · doi:10.48550/arxiv.1412.7449 · Cited by 464 · see citing works

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1179 · Cited by 434 · see citing works

On the naturalness of software

Hindle, A., Barr, E. T., Su, Z., Gabel, M., & Devanbu, P. (2012) · 2012 34th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2012.6227135 · Cited by 425 · see citing works

Designing and Interpreting Probes with Control Tasks

Hewitt, J., & Liang, P. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1275 · Cited by 393 · see citing works

Hierarchical Multiscale Recurrent Neural Networks

Chung, J., Ahn, S., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.01704 · Cited by 386 · see citing works

The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing

Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1128 · Cited by 384 · see citing works

Revisiting readability: a unified framework for predicting text quality

Pitler, E., & Nenkova, A. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613742 · Cited by 375 · see citing works

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Kates-Harbeck, J., Svyatkovskiy, A., & Tang, W. (2019) · Nature · doi:10.1038/s41586-019-1116-4 · Cited by 367 · see citing works

Spoken Language Derived Measures for Detecting Mild Cognitive Impairment

Roark, B., Mitchell, M., Hosom, J.-P., Hollingshead, K., & Kaye, J. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2112351 · Cited by 355 · see citing works

Datasets: A Community Library for Natural Language Processing

Lhoest, Q., Villanova del Moral, A., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., et al. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2021.emnlp-demo.21 · Cited by 343 · see citing works

Learning to Generate Reviews and Discovering Sentiment

Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1704.01444 · Cited by 330 · see citing works

Computational analyses of multilevel discourse comprehension

Graesser, A. C., & McNamara, D. S. (2011) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01081.x · Cited by 325 · see citing works

On the naturalness of software

Hindle, A., Barr, E. T., Gabel, M., Su, Z., & Devanbu, P. (2016) · Communications of the ACM · doi:10.1145/2902362 · Cited by 307 · see citing works

Sharp nearby, fuzzy far away: How neural language models use context

Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1027 · Cited by 303 · see citing works

A Survey on Model Compression for Large Language Models

Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00704 · Cited by 273 · see citing works

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (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.324 · Cited by 254 · see citing works

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215 · see citing works

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852172 · Cited by 240 · see citing works

The state of the art of deep learning models in medical science and their challenges

Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222 · see citing works

Measuring the Evolution of a Scientific Field through Citation Frames

Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00028 · Cited by 212 · see citing works

Learning to Write with Cooperative Discriminators

Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., & Choi, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1152 · Cited by 192 · see citing works

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

MEMORY-BASED LANGUAGE PROCESSING: Psycholinguistic Research in the 1990s

McKoon, G., & Ratcliff, R. (1998) · Annual Review of Psychology · doi:10.1146/annurev.psych.49.1.25 · Cited by 179 · see citing works

Bayesian Recurrent Neural Networks

Fortunato, M., Blundell, C., & Vinyals, O. (2017) · arXiv · doi:10.48550/arxiv.1704.02798 · Cited by 145 · see citing works

Searching by Talking: Analysis of Voice Queries on Mobile Web Search

Guy, I. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911525 · Cited by 140 · 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

A Systematic Assessment of Syntactic Generalization in Neural Language Models

Hu, J., Gauthier, J., Qian, P., Wilcox, E., & Levy, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.158 · Cited by 134 · see citing works

A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

Loyola, P., Marrese-Taylor, E., & Matsuo, Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2045 · Cited by 128 · see citing works

Mixed-Precision Deep Learning Based on Computational Memory

Nandakumar, S. R., Le Gallo, M., Piveteau, C., Joshi, V., Mariani, G., Boybat, I., Karunaratne, G., Khaddam-Aljameh, R., Egger, U., Petropoulos, A., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00406 · Cited by 120 · see citing works

A computational study of mental health awareness campaigns on social media

Saha, K., Torous, J., Ernala, S. K., Rizuto, C., Stafford, A., & De Choudhury, M. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz028 · Cited by 93 · see citing works

We Need to Talk about Standard Splits

Gorman, K., & Bedrick, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1267 · Cited by 99 · see citing works

A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders

Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96 · see citing works

What can AI do for me?: evaluating machine learning interpretations in cooperative play

Feng, S., & Boyd-Graber, J. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302265 · Cited by 92 · see citing works

Analyzing Information Leakage of Updates to Natural Language Models

Zanella-Béguelin, S., Wutschitz, L., Tople, S., Rühle, V., Paverd, A., Ohrimenko, O., Köpf, B., & Brockschmidt, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417880 · Cited by 86 · see citing works

The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries

Guy, I. (2018) · ACM Transactions on Information Systems · doi:10.1145/3182163 · Cited by 71 · see citing works

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., et al. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.1 · Cited by 74 · see citing works

Dependency parsing of learner English

Huang, Y., Murakami, A., Alexopoulou, T., & Korhonen, A. (2018) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.16080.hua · Cited by 68 · see citing works

Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training

Hosseini, E. A., Schrimpf, M., Zhang, Y., Bowman, S., Zaslavsky, N., & Fedorenko, E. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00137 · Cited by 63 · see citing works

Equity Beyond Bias in Language Technologies for Education

Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Román, E., & Black, A. W. (2019) · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4446 · Cited by 62 · see citing works

Using Learning Analytics for Preserving Academic Integrity

Amigud, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldan, A.-E. (2017) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v18i5.3103 · Cited by 61 · see citing works

Localizing syntactic predictions using recurrent neural network grammars

Brennan, J. R., Dyer, C., Kuncoro, A., & Hale, J. T. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107479 · Cited by 58 · see citing works

Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.102 · Cited by 58 · see citing works

Using automatic assessment of speech production to predict current and future cognitive function in older adults

Ostrand, R., & Gunstad, J. (2021) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/0891988720933358 · Cited by 49 · see citing works

Probabilistic parsing and psychological plausibility

Brants, T., & Crocker, M. (2000) · Proceedings of the 18th conference on Computational linguistics - · doi:10.3115/990820.990837 · Cited by 48 · see citing works

Measuring text similarity based on structure and word embedding

Farouk, M. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.04.002 · Cited by 47 · see citing works

rTop-<i>k</i>: A Statistical Estimation Approach to Distributed SGD

Barnes, L. P., Inan, H. A., Isik, B., & Ozgur, A. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.3042094 · Cited by 44 · see citing works

ReCode: Robustness Evaluation of Code Generation Models

Wang, S., Li, Z., Qian, H., Yang, C., Wang, Z., Shang, M., Kumar, V., Tan, S., Ray, B., Bhatia, P., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.773 · Cited by 42 · see citing works

Automated analysis of lexical features in frontotemporal degeneration

Cho, S., Nevler, N., Ash, S., Shellikeri, S., Irwin, D. J., Massimo, L., Rascovsky, K., Olm, C., Grossman, M., & Liberman, M. (2021) · Cortex · doi:10.1016/j.cortex.2021.01.012 · Cited by 41 · see citing works

Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

Garimella, A., Banea, C., Hovy, D., & Mihalcea, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1339 · Cited by 41 · see citing works

Extracting and Ranking Travel Tips from User-Generated Reviews

Guy, I., Mejer, A., Nus, A., & Raiber, F. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052632 · Cited by 32 · see citing works

Emerging Trends: SOTA-Chasing

Church, K. W., & Kordoni, V. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000043 · Cited by 28 · see citing works

What if This Modified That? Syntactic Interventions with Counterfactual Embeddings

Tucker, M., Qian, P., & Levy, R. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.76 · Cited by 16 · see citing works

New Initiative: The Naturalness of Software

Devanbu, P. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.190 · Cited by 9 · see citing works

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs

Dong, P., Li, L., Zhong, Y., Du, D., Fan, R., Chen, Y., Tang, Z., Wang, Q., Xue, W., Guo, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2408.01803 · Cited by 1 · see citing works