Papers Unsupervised Text Style Transfer
“Unsupervised Text Style Transfer” 태그가 달린 논문 42편 · 필터 해제
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning regards selecting appropriate keywords incl…
Few-Shot Text ClassificationQ-LearningReinforcement Learning (RL)Style Transfer+4Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions
Unsupervised Text Style Transfer (UTST) has emerged as a critical task within the domain of Natural Language Processing (NLP), aiming to transfer one stylistic aspect of a sentence into another style without changing its…
In-Context LearningKnowledge DistillationSentenceStyle Transfer+2Prefix-Tuning Based Unsupervised Text Style Transfer
Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In this paper, we employ powerful pre-trai…
SentenceStyle TransferText Style TransferUnsupervised Text Style TransferUnsupervised Text Style Transfer with Deep Generative Models
We present a general framework for unsupervised text style transfer with deep generative models. The framework models each sentence-label pair in the non-parallel corpus as partially observed from a complete quadruplet w…
SentenceStyle TransferText Style TransferUnsupervised Text Style TransferMSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer
Unsupervised text style transfer task aims to rewrite a text into target style while preserving its main content. Traditional methods rely on the use of a fixed-sized vector to regulate text style, which is difficult to …
Style TransferText Style TransferUnsupervised Text Style TransferStyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer
Text style transfer aims to alter the style of a sentence while preserving its content. Due to the lack of parallel corpora, most recent work focuses on unsupervised methods and often uses cycle construction to train mod…
Data AugmentationDecoderDisentanglementSentence+3Composable Text Controls in Latent Space with ODEs
Real-world text applications often involve composing a wide range of text control operations, such as editing the text w.r.t. an attribute, manipulating keywords and structure, and generating new text of desired properti…
AttributeLanguage ModelingLanguage ModellingText Style Transfer+1Don’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+1Low Resource Style Transfer via Domain Adaptive Meta Learning
Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of non-parallel data t…
General KnowledgeLanguage ModelingLanguage ModellingMeta-Learning+3RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
Prompting has shown impressive success in enabling large pretrained language models (LMs) to perform diverse NLP tasks, especially when only few downstream data are available. Automatically finding the optimal prompt for…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Style Transfer+2So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content of text being translated from source to target domain. However, it does not explicitly ma…
AttributeData AugmentationGenerative Adversarial NetworkStyle Transfer+2Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer
Recent studies show that auto-encoder based approaches successfully perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. T…
DenoisingSentenceStyle TransferText Generation+2Efficient Reinforcement Learning for Unsupervised Controlled Text Generation
Controlled text generation tasks such as unsupervised text style transfer have increasingly adopted the use of Reinforcement Learning (RL). A major challenge in applying RL to such tasks is the sparse reward, which is av…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Style Transfer+3Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning
Text style transfer is a challenging text generation problem, which aims at altering the style of a given sentence to a target one while keeping its content unchanged. Since there is a natural scarcity of parallel datase…
Adversarial AttackContrastive LearningSentenceStyle Transfer+3Low Resource Style Transfer via Domain Adaptive Meta Learning
Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of nonparallel data t…
General KnowledgeLanguage ModelingLanguage ModellingMeta-Learning+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+1DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning
Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of nonparallel data to…
General KnowledgeLanguage ModelingLanguage ModellingMeta-Learning+3Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer
Unsupervised text style transfer aims to alter the underlying style of the text to a desired value while keeping its style-independent semantics, without the support of parallel training corpora. Existing methods struggl…
AttributeDecoderKnowledge DistillationStyle Transfer+2Exploring Non-Autoregressive Text Style Transfer
In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer. We first propose a base NAR model by directly adapting the common training scheme from its AutoRegressive (AR) counterpart…
Contrastive LearningKnowledge DistillationStyle TransferText Style Transfer+1Transductive Learning for Unsupervised Text Style Transfer
Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to…
DecoderInductive LearningRetrievalStyle Transfer+3