Described herein are embodiments of a framework, referred to as a decomposable variational autoencoder (DecVAE), that unwraps grammar and semantics by using a total correlation penalty of Kullback-Leibler (KL) divergence. The KL divergence terms of the original VAE are decomposed so that the generated hidden variables can be separated in a clear and interpretable manner. Embodiments of a DecVAE model are evaluated for various semantic similarity and syntactic similarity data sets. Experimental results show that the embodiment of the DecVAE model realizes the performance of the prior art (SOTA) in the aspect of unwrapping processing between syntactic representation and semantic representation.
Segmented grammar and semantics in decomposable variational autoencoder sentence representation