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Preventing Posterior Collapse with DVAE for Text Modeling.

Tianbao Song¹, Zongyi Huang¹, Xin Liu²

¹School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study presents DVAE, a novel variational autoencoder for text modeling that prevents posterior collapse. DVAE uses a dual-path decoder and KL weight dropout for improved density estimation and text generation.

Keywords:

latent variable posterior collapse text modeling variational autoencoder

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Area of Science:

Artificial Intelligence
Natural Language Processing
Machine Learning

Background:

Posterior collapse is a common issue in variational autoencoders for text modeling.
Existing models struggle to effectively capture latent representations in text data.

Purpose of the Study:

Introduce a novel variational autoencoder, DVAE, to address posterior collapse in text modeling.
Enhance the quality of text generation and representation learning.

Main Methods:

DVAE employs a dual-path decoder architecture (Path A and Path B).
Path B masks input tokens to encourage latent variable encoding.
A stopping strategy removes Path B during training, and KL weight dropout is used.

Main Results:

DVAE effectively prevents posterior collapse in text modeling.
The model demonstrates superior performance in density estimation and representation learning.
Experimental results confirm DVAE's capability in high-quality text generation.

Conclusions:

DVAE offers a robust solution to posterior collapse in variational autoencoders for text.
The proposed methods enhance latent variable expressiveness and model performance.
DVAE shows significant potential for advancing text modeling research.