Decomposing Textual Information For Style Transfer
arXiv:1909.12928 · doi:10.18653/v1/D19-5613
Abstract
This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.
arXiv commentarXiv admin note: substantial text overlap with arXiv:1908.06809
References in corpus (9)
- Style Transfer from Non-Parallel Text by Cross-Alignment
- Style Transfer as Unsupervised Machine Translation
- On Unifying Deep Generative Models
- Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
- Multiple-Attribute Text Style Transfer
- Evaluating prose style transfer with the Bible
- Structured Content Preservation for Unsupervised Text Style Transfer
- Generating lyrics with variational autoencoder and multi-modal artist embeddings
- What is wrong with style transfer for texts?
Cited by in corpus (4)
- Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric
- From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey
- Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces
- Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity