Style Extraction on Text Embeddings Using VAE and Parallel Dataset
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Text GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets often used for training these representatio…
Contrastive LearningLanguage ModelingLanguage ModellingLarge Language ModelStoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing
Non-parallel text style transfer is an important task in natural language generation. However, previous studies concentrate on the token or sentence level, such as sentence sentiment and formality transfer, but neglect l…
SentenceStyle TransferText GenerationText Style TransferUnsupervised Text Style Transfer with Content Embeddings
The style transfer task (here style is used in a broad “authorial” sense with many aspects including register, sentence structure, and vocabulary choice) takes text input and rewrites it in a specified target style prese…
Language ModelingLanguage ModellingMachine TranslationSentence+4VAE based Text Style Transfer with Pivot Words Enhancement Learning
Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content. Due to the scarcity of high-quality parallel training data, unsupervised learning …
Style TransferText Style TransferRevisiting Embeddings for Graph Neural Networks
Current graph representation learning techniques use Graph Neural Networks (GNNs) to extract features from dataset embeddings. In this work, we examine the quality of these embeddings and assess how changing them can aff…
Graph Representation LearningRepresentation Learningtext-classificationText Classification