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Synapse
May 24, 20260 citationsOpen Access

Implicit Semantic Attention

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GTGary Nan Tie

Key Points

  • Evaluate how implicit deep learning models can capture the interplay between syntax and semantics in language processing.
  • Utilized implicit deep learning models to learn Q-K-V embeddings and contextual token representations.
  • Explored six parameterization methods representing different interactions between syntax and semantics.
  • Demonstrated that different parameterizations distinctly inform the nuances of syntax and semantics.
  • Findings suggest that these models may reflect various cognitive processing regimes in language.

Abstract

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.

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Cite This Study

Gary Nan Tie (2026) studied this question.

synapsesocial.com/papers/6a12949848a0ea1665671037https://doi.org/10.13140/rg.2.2.11218.31685
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