FET-LM: Flow-Enhanced Variational Autoencoder for Topic-Guided Language Modeling.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Variational Autoencoders (VAEs) are common for unsupervised text generation.
• Standard VAEs assume simple isotropic Gaussian distributions for text, which is often inaccurate.
• Real-world text exhibits complex, diverse distributions due to varying semantic topics.

Purpose of the Study:

• To propose a novel flow-enhanced VAE (FET-LM) for topic-guided language modeling.
• To address the limitations of VAEs in modeling complex text distributions.
• To improve the quality and semantic consistency of generated text guided by topics.

Main Methods:

• FET-LM models topic and sequence latent spaces separately.
• Utilizes normalized flow with householder transformations for sophisticated sequence posterior modeling.
• Incorporates a neural latent topic component, leveraging sequence knowledge for unsupervised topic learning.
• Employs the topic encoder as a discriminator to enhance topic-text correlation.

Main Results:

• FET-LM effectively models intricate text distributions beyond simple isotropic Gaussians.
• The model learns interpretable representations for both sequence and topic.
• Generated paragraphs demonstrate high quality and strong semantic consistency with assigned topics.
• Achieved encouraging results across various automatic metrics and generation tasks.

Conclusions:

• FET-LM offers a powerful approach for topic-guided language generation.
• The proposed architecture accurately models complex text distributions and learns meaningful latent representations.
• FET-LM successfully generates semantically coherent and topic-relevant text, outperforming existing methods.