Papers Unsupervised Text Style Transfer
“Unsupervised Text Style Transfer” 태그가 달린 논문 42편 · 필터 해제
Unsupervised 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+4Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer
Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak t…
AttributecounterfactualDisentanglementSentence+3Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequen…
Machine TranslationStyle TransferText GenerationText Style Transfer+2So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
Transferring text from one domain to the other has seen tremendous progress in the recent past. However, these methods do not aim to explicitly maintain constraints such as similar text length, descriptiveness between th…
AttributeGenerative Adversarial NetworkStyle TransferText Style Transfer+1NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer
Autoregressive models have been widely used in unsupervised text style transfer. Despite their success, these models still suffer from the content preservation problem that they usually ignore part of the source sentence…
SentenceStyle TransferText Style TransferUnsupervised Text Style Transfer+1LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coarse-to-fine editor for style transfer tha…
Style TransferText Style TransferUnsupervised Text Style TransferSE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer
Text style transfer aims to change the style of sentences while preserving the semantic meanings. Due to the lack of parallel data, the Denoising Auto-Encoder (DAE) is widely used in this task to model distributions of d…
DenoisingSentenceStyle TransferText Style Transfer+1How Positive Are You: Text Style Transfer using Adaptive Style Embedding
The prevalent approach for unsupervised text style transfer is disentanglement between content and style. However, it is difficult to completely separate style information from the content. Other approaches allow the lat…
DisentanglementSentenceStyle TransferText Style Transfer+1Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer
Text style transfer aims to change an input sentence to an output sentence by changing its text style while preserving the content. Previous efforts on unsupervised text style transfer only use the surface features of wo…
SentenceStyle TransferText Style TransferUnsupervised Text Style TransferSemi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization
Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences, which can be used to improve performance of many downstream NLP tasks. In this work, we propose a semi-supe…
Formality Style TransferLanguage ModelingLanguage ModellingSemi-Supervised Formality Style Transfer+4Unsupervised Text Style Transfer with Padded Masked Language Models
We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases when no parallel source-target pairs are available, we train masked language models (MLMs) for both the source and the target dom…
SentenceSentence FusionStyle TransferText Style Transfer+1Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach to unsupervised text style transfer, whi…
Style TransferText Style TransferUnsupervised Text Style TransferLearning Implicit Text Generation via Feature Matching
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN …
Conditional Text GenerationStyle TransferText GenerationText Style Transfer+1A Probabilistic Formulation of Unsupervised Text Style Transfer
We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially obse…
DeciphermentLanguage ModellingMachine TranslationStyle Transfer+5A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) g…
SentenceStyle TransferText Style TransferUnsupervised Text Style TransferRevision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesome adversarial learning. In this paper, w…
AttributeDisentanglementSentenceStyle Transfer+2A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Unsupervised text style transfer aims to transfer the underlying style of text but keep its main content unchanged without parallel data. Most existing methods typically follow two steps: first separating the content fro…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Text Style Transfer+1Formality Style Transfer with Hybrid Textual Annotations
Formality style transformation is the task of modifying the formality of a given sentence without changing its content. Its challenge is the lack of large-scale sentence-aligned parallel data. In this paper, we propose a…
Formality Style TransferSentenceStyle TransferText Style Transfer+1Structured Content Preservation for Unsupervised Text Style Transfer
Text style transfer aims to modify the style of a sentence while keeping its content unchanged. Recent style transfer systems often fail to faithfully preserve the content after changing the style. This paper proposes a …
Language ModelingLanguage ModellingSentenceStyle Transfer+2Unsupervised Text Style Transfer using Language Models as Discriminators
Binary classifiers are often employed as discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with this approa…
DeciphermentLanguage ModelingLanguage ModellingStyle Transfer+3