Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder

Formal/symbolic semantics can provide canonical, rigid controllability and interpretability to sentence representations due to their $\mathit{\text{localisation}}$ or $\mathit{\text{composition}}$ property. How can we deliver such property to the current distributional sentence representations to control and interpret the generation of language models (LMs)? In this work, we theoretically frame the sentence semantics as the composition of $\mathit{\text{semantic}}\mathit{\text{role}}\mathit{\text{-}}\mathit{\text{word}}\mathit{\text{content}}$ features and propose the formal semantic geometry. To inject such geometry into Transformer-based LMs (i.e. GPT2), we deploy Transformer-based Variational AutoEncoder with a supervision approach, where the sentence generation can be manipulated and explained over low-dimensional latent Gaussian space. In addition, we propose a new probing algorithm to guide the movement of sentence vectors over such geometry. Experimental results reveal that the formal semantic geometry can potentially deliver better control and interpretation to sentence generation.