I am a Ph.D. student in the Department of Cognitive Science at Johns Hopkins University, co-advised by Tal Linzen and Paul Smolensky. Before coming to JHU, I received a B.A. in Linguistics at Yale University, advised by Robert Frank.
I study computational linguistics using techniques from machine learning, natural language processing, and cognitive science. My research focuses on how to achieve robust generalization in models of language, as this remains one of the main areas where current AI systems fall short. In particular, I study which inductive biases and which representations of structure enable robust generalization, since these are two of the major components that determine how models generalize to novel types of input.
- R. Thomas McCoy, Ellie Pavlick, and Tal Linzen. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. ACL 2019.[pdf]
- R. Thomas McCoy, Robert Frank, and Tal Linzen. Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks. Proceedings of the 40th Annual Conference of the Cognitive Science Society. [pdf]
- R. Thomas McCoy, Tal Linzen, Ewan Dunbar, and Paul Smolensky. RNNs implicitly implement Tensor Product Representations. ICLR 2019. [pdf] [demo]