| PaLM: Scaling Language Modeling with Pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... arXiv preprint arXiv:2204.02311, 2022 | 929 | 2022 |
| Lamda: Language models for dialog applications R Thoppilan, D De Freitas, J Hall, N Shazeer, A Kulshreshtha, HT Cheng, ... arXiv preprint arXiv:2201.08239, 2022 | 364 | 2022 |
| Language from police body camera footage shows racial disparities in officer respect R Voigt, NP Camp, V Prabhakaran, WL Hamilton, RC Hetey, CM Griffiths, ... Proceedings of the National Academy of Sciences 114 (25), 6521-6526, 2017 | 354 | 2017 |
| Computational Argumentation Quality Assessment in Natural Language H Wachsmuth, N Naderi, Y Hou, Y Bilu, V Prabhakaran, TA Thijm, G Hirst, ... | 176 | 2017 |
| Social Biases in NLP Models as Barriers for Persons with Disabilities B Hutchinson, V Prabhakaran, E Denton, K Webster, Y Zhong, S Denuyl ACL 2020, 2020 | 167 | 2020 |
| Committed belief annotation and tagging MT Diab, L Levin, T Mitamura, O Rambow, V Prabhakaran, W Guo Proceedings of the Third Linguistic Annotation Workshop (LAW), 68-73, 2009 | 99 | 2009 |
| Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations AM Davani, M Díaz, V Prabhakaran Transactions of the Association for Computational Linguistics 10, 92-110, 2022 | 93 | 2022 |
| Re-imagining algorithmic fairness in india and beyond N Sambasivan, E Arnesen, B Hutchinson, T Doshi, V Prabhakaran Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 90 | 2021 |
| Perturbation Sensitivity Analysis to Detect Unintended Model Biases V Prabhakaran, B Hutchinson, M Mitchell EMNLP, 2019 | 79 | 2019 |
| Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing V Prabhakaran, WL Hamilton, D McFarland, D Jurafsky | 71 | 2016 |
| Automatic committed belief tagging V Prabhakaran, O Rambow, M Diab 23rd International Conference on Computational Linguistics (COLING): Posters …, 2010 | 71 | 2010 |
| RtGender: A corpus for studying differential responses to gender R Voigt, D Jurgens, V Prabhakaran, D Jurafsky, Y Tsvetkov Proceedings of the Eleventh International Conference on Language Resources …, 2018 | 68 | 2018 |
| On Releasing Annotator-Level Labels and Information in Datasets V Prabhakaran, AM Davani, M Díaz The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing …, 2021 | 55 | 2021 |
| Predicting Overt Display of Power in Written Dialogs V Prabhakaran, O Rambow, M Diab Human Language Technologies: The 2012 Annual Conference of the NAACL (North …, 2012 | 53 | 2012 |
| Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics D Martin Jr, V Prabhakaran, J Kuhlberg, A Smart, WS Isaac ICLR Workshop on Machine Learning in Real Life, 2020 | 47 | 2020 |
| Bias and fairness in natural language processing KW Chang, V Prabhakaran, V Ordonez Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 47 | 2019 |
| Gender and Power: How Gender and Gender Environment Affect Manifestations of Power V Prabhakaran, EE Reid, O Rambow Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 39 | 2014 |
| Predicting Power Relations between Participants in Written Dialog from a Single Thread V Prabhakaran, O Rambow ACL 2014 (short papers), 2014 | 33 | 2014 |
| Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing V Prabhakaran, M Bloodgood, M Diab, BJ Dorr, L Levin, C Piatko, ... Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM …, 2012 | 32 | 2012 |
| Unintended Machine Learning Biases as Social Barriers for Persons with Disabilities B Hutchinson, E Denton, K Webster, SC Denuyl, V Prabhakaran, Y Zhong ASSETS Workshop on AI Fairness for People with Disabilities, 2019 | 30 | 2019 |
Owen RambowStony Brook UniversityVerified email at stonybrook.edu
