Jackie C. K. Cheung is a Canada CIFAR AI Chair at Mila and an associate professor in the School of Computer Science at McGill University.
Cheung conducts research in natural language processing, an area of artificial intelligence in which we build computational models of human languages such as English or French. His research group’s goal is to develop computational methods for understanding text and speech, in order to generate language that is fluent and appropriate to the context. In his lab, Cheung investigates statistical machine learning techniques for analyzing and making predictions about language. Several current projects include summarizing fiction, extracting events from text, and adapting language across genres.
- Best Paper Award Association for Computational Linguistics, 2018
- Facebook Fellowship, 2013
- NSERC Alexander Graham Bell Canada Graduate Scholarship, 2012
Emami, A., Porada, I., Olteanu, A., Suleman, K., Trischler, A., & Cheung, J. C. K. (2021). ADEPT: An Adjective-Dependent Plausibility Task.
Xu, P., Kumar, D., Yang, W., Zi, W., Tang, K., Huang, C., … & Cao, Y. (2021). Optimizing deeper transformers on small datasets.
Wu, J., Xu, Y., Zhang, Y., Ma, C., Coates, M., & Cheung, J. C. K. (2021). TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion.
Porada, I., Suleman, K., Trischler, A., & Kit Cheung, J. C. (2021). Modeling Event Plausibility with Consistent Conceptual Abstraction.
Socolof, M., Cheung, J. C. K., Wagner, M., & O’Donnell, T. J. (2021). Characterizing Idioms: Conventionality and Contingency.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.