Total Correlation Variational Autoencoder for Syntax Semantics Disentanglement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep generative models lack fine-grained decomposition in segmenting syntax from semantics, leading to inadequate separation of these components in sentences.
Innovation Solution
The introduction of total correlation into a variational autoencoder (VAE) as a penalty term enables deeper and more meaningful factorization of hidden variables, allowing for improved segmentation of syntax and semantics through the use of a multi-task generative model with latent variables that are decomposable and guided by attention networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep generative model with von Mises Fisher and Gaussian priors is used for syntax-semantics segmentation, then the model can separate latent variables with a generative approach, but it lacks fine-grained decomposition and fails to segment syntax from semantics in a subtle way
Solution Approach 1:
The patent applies segmentation by introducing a total correlation penalty term that decomposes the joint distribution of latent variables into finer-grained components. This penalty term encourages the model to separate syntax and semantics into more distinct and fine-grained latent factors, moving beyond the coarse segmentation achieved by simple prior distributions. The total correlation measure quantifies the dependence among multiple latent variables and penalizes their mutual information, thereby achieving finer decomposition.
Solution Approach 2:
The patent changes the parameterization of the latent variable distributions by using total correlation as a penalty term in the variational lower bound. This parameter change transforms the optimization objective to enforce finer-grained disentanglement. Specifically, the total correlation penalty modifies the likelihood function to penalize configurations where latent variables are highly correlated, thereby encouraging finer separation of semantic and syntactic factors.
2Loss of information
If total correlation is introduced as a penalty term in VAE to enable deeper factorization, then fine-grained decomposition is achieved, but the computational complexity and model structure become more complex
Solution Approach 1:
The patent introduces the total correlation penalty term as an intermediary mechanism that mediates between the latent variables and the optimization objective. This penalty term acts as a bridge that translates the goal of fine-grained decomposition into a computable constraint. By using total correlation as an intermediary measure of dependence, the model can enforce information retention while managing computational complexity through a well-defined mathematical penalty.
Solution Approach 2:
The total correlation penalty term provides feedback during training by continuously measuring the dependence among latent variables and adjusting the optimization to reduce this dependence. This feedback mechanism guides the model toward finer-grained factorization by penalizing configurations where latent variables remain correlated, thereby iteratively improving the disentanglement quality throughout the training process.
Data Source
AI summary
Described herein are embodiments of a framework named as total correlation variational autoencoder (TC_VAE) to disentangle syntax and semantics by making use of total correlation penalties of KL divergences. One or more Kullback-Leibler (KL) divergence terms in a loss for a variational autoencoder are discomposed so that generated hidden variables may be separated. Embodiments of the TC_VAE framework were examined on semantic similarity tasks and syntactic similarity tasks. Experimental results show that better disentanglement between syntactic and semantic representations have been achieved compared with state-of-the-art (SOTA) results on the same data sets in similar settings.