Implicit deep learning models are used to jointly learn Q-K-V embeddings and contextual token representations designed to simultaneously capture language syntactic co-occurrence and semantic alignment. Six kinds of parameterization characterize different subtle and nuanced ways syntax and semantics can interact. They may be construed as different cognitive regimes.
Gary Nan Tie (Thu,) studied this question.