用变分自编码器分析圣经译本风格差异，识别出不同版本的独特语言特征。
Style Extraction on Text Embeddings Using VAE and Parallel Dataset
- 通过VAE将文本嵌入高维向量，捕捉译本间的风格分布差异。
- 模型能有效区分美国标准版（ASV）与其他译本的风格，识别准确率较高。
- 适用于AI文本生成与风格分析，尤其适合多版本文本对比研究。
本研究利用变分自编码器（VAE）模型分析不同圣经译本之间的风格差异。通过将文本数据嵌入高维向量，旨在检测并解析译本间的风格变化，重点聚焦于美国标准版（ASV）与其他译本的区别。结果表明，每个译本均呈现出独特的风格分布，可被VAE模型有效识别。这说明该模型在捕捉和区分文本风格方面具有较强能力，尽管其主要优化目标是区分单一风格。研究揭示了模型在基于AI的文本生成与风格分析中的应用潜力，同时也指出需进一步改进以应对多维度风格关系的复杂性。未来研究可将此方法拓展至其他文本领域，深入挖掘各类文本数据中蕴含的风格特征。
原文摘要 · Abstract (English)
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.
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