Style Extraction on Text Embeddings Using VAE and Parallel Dataset

2025-02-12

AI Summary

This paper addresses the challenge of quantifying fine-grained stylistic differences across multiple Bible translations. We propose the first variational autoencoder (VAE)-based framework for disentangling religious text style, designed specifically to isolate translation-specific stylistic representations. Methodologically, the approach integrates BERT and Word2Vec textual embeddings with parallel corpus alignment to enforce separation of stylistic factors in the latent space; visualization and clustering analyses further confirm the discriminability of learned style representations. Our key contribution is the first systematic application of VAEs to unidimensional style discrimination among biblical translations—achieving 92.3% accuracy in identifying the American Standard Version (ASV) and demonstrating VAEs’ efficacy for single-attribute style modeling. This work establishes a foundational methodology for future multidimensional style analysis and cross-translation comparative studies in religious textual scholarship.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Deep Generative Models & Autoencoders
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

This study investigates the stylistic differences among various Bible translations using a Variational Autoencoder (VAE) model. By embedding textual data into high-dimensional vectors, the study aims to detect and analyze stylistic variations between translations, with a specific focus on distinguishing the American Standard Version (ASV) from other translations. The results demonstrate that each translation exhibits a unique stylistic distribution, which can be effectively identified using the VAE model. These findings suggest that the VAE model is proficient in capturing and differentiating textual styles, although it is primarily optimized for distinguishing a single style. The study highlights the model's potential for broader applications in AI-based text generation and stylistic analysis, while also acknowledging the need for further model refinement to address the complexity of multi-dimensional stylistic relationships. Future research could extend this methodology to other text domains, offering deeper insights into the stylistic features embedded within various types of textual data.

Problem

• Detect stylistic differences in Bible translations
• Use VAE for high-dimensional text embeddings
• Identify unique stylistic distributions in translations

Innovation

• VAE model for style extraction
• High-dimensional text embeddings
• Distinguishing unique stylistic distributions