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arXiv · 2506.20083

Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder

Yingji Zhang·Danilo S. Carvalho·André Freitas
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Abstract

Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, compositionality, and generalisation capabilities of Transformer-based auto-regressive language models (LMs). In this survey, we offer a novel perspective on latent space geometry through the lens of compositional semantics, a direction we refer to as \textit{semantic representation learning}. This direction enables a bridge between symbolic and distributional semantics, helping to mitigate the gap between them. We review and compare three mainstream autoencoder architectures-Variational AutoEncoder (VAE), Vector Quantised VAE (VQVAE), and Sparse AutoEncoder (SAE)-and examine the distinctive latent geometries they induce in relation to semantic structure and interpretability.

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Yingji Zhang, Danilo S. Carvalho, André Freitas. 2025-06-25. Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder. https://arxiv.org/abs/2506.20083

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