Yejin Choi (Korean: 최예진; born 1977)^[1] is the Wissner-Slivka Chair of Computer Science at the University of Washington. Her research considers natural language processing and computer vision.

Choi is from South Korea. She attended Seoul National University.^[2] After earning a bachelor's degree in Computer Science, Choi moved to the United States, where she joined Cornell University as a graduate student. There she worked with Claire Cardie on natural language processing. After earning her doctorate, Choi joined Stony Brook University as an Assistant Professor of Computer Science.^[3] At Stony Brook University Choi developed a statistical technique to identify fake hotel reviews.^[4]

In 2018 Choi joined the Allen Institute for AI.^[5] Her research looks to endow computers with a statistical understanding of written language.^[6] She became interested in neural networks and their application in artificial intelligence. She started to assemble a knowledge base that became known as the atlas of machine commonsense (ATOMIC). By the time she had finished the creation of ATOMIC, the language model generative Pre-trained Transformer 2 (GPT-2) had been released.^[7] ATOMIC does not make use of linguistic rules, but combines the representations of different languages within a neural network.^[7]

In 2020, Choi was endowed with the Brett Helsel Professorship, which she held until her became Chair of Computer Science in 2023.^[8][9] She has since made use of Commonsense Transformers (COMET) with Good old fashioned artificial intelligence (GOFAI). The approach combines symbolic reasoning and neural networks.^[7] She has developed computational models that can detect biases in language that work against people from underrepresented groups.^[10] For example, one study demonstrated that female film characters are portrayed as less powerful than their male counterparts.^[6]

In 2023, Choi became The Wissner-Slivka Chair of Computer Science.^[9] Choi is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others.^[11]

• 2013 International Conference on Computer Vision Marr Prize^[12][13]
• 2016 Institute of Electrical and Electronics Engineers AI One to Watch^[12]
• 2017 Facebook ParlAI Research Award^[14]
• 2018 Anita Borg Early Career Award^[10]
• 2020 Association for the Advancement of Artificial Intelligence Outstanding Paper Award^[15]
• 2021 Conference on Neural Information Processing Systems Outstanding Paper Award^[16]
• 2021 Association for Computational Linguistics Test-of-time Paper Award^[17]
• 2021 Conference on Computer Vision and Pattern Recognition Longuet-Higgins Prize^[18]
• 2022 North American Chapter of the Association for Computational Linguistics Best Paper Award^[19]
• 2022 International Conference on Machine Learning Outstanding Paper Award^[20]
• 2022 MacArthur Fellowship^[21]
• 2023 Association for Computational Linguistics Best Paper Award^[22]

• Ott, Myle; Choi, Yejin; Cardie, Claire; Hancock, Jeffrey T. (2011). "Finding Deceptive Opinion Spam by Any Stretch of the Imagination". Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, USA: Association for Computational Linguistics: 309–319. arXiv:1107.4557. Bibcode:2011arXiv1107.4557O. ISBN 9781932432879. S2CID 2510724.
• Kulkarni, Girish; Premraj, Visruth; Ordonez, Vicente; Dhar, Sagnik; Li, Siming; Choi, Yejin; Berg, Alexander C.; Berg, Tamara L. (2013). "BabyTalk: Understanding and Generating Simple Image Descriptions". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (12): 2891–2903. CiteSeerX 10.1.1.225.5228. doi:10.1109/TPAMI.2012.162. ISSN 1939-3539. PMID 22848128.
• Choi, Yejin; Cardie, Claire; Riloff, Ellen; Patwardhan, Siddharth (2005). "Identifying sources of opinions with conditional random fields and extraction patterns". Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. Morristown, NJ, USA: Association for Computational Linguistics. pp. 355–362. doi:10.3115/1220575.1220620.