Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
Disentangling the content and style in the latent space is prevalent in unpaired text style transfer. However, two major issues exist in most of the current neural models. 1) It is difficult to completely strip the style information from the semantics for a sentence. 2) The recurrent neural network (RNN) based encoder and decoder, mediated by the latent representation, cannot well deal with the issue of the long-term dependency, resulting in poor preservation of non-stylistic semantic content. In this paper, we propose the Style Transformer, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.
Code (4)
Tasks
DecoderSentenceStyle TransferText Style TransferMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Mask and Regenerate: A Classifier-based Approach for Unpaired Sentiment Transformation of Reviews for Electronic Commerce Websites.
Style transfer is the task of transferring a sentence into the target style while keeping its content. The major challenge is that parallel corpora are not available for various domains. In this paper, we propose a Mask-…
Language ModelingLanguage ModellingSentenceStyle TransferIn-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
Large-scale noisy web image-text datasets have been proven to be efficient for learning robust vision-language models. However, when transferring them to the task of video retrieval, models still need to be fine-tuned on…
RetrievalStyle TransferVideo RetrievalMulti-Reference Neural TTS Stylization with Adversarial Cycle Consistency
Current multi-reference style transfer models for Text-to-Speech (TTS) perform sub-optimally on disjoints datasets, where one dataset contains only a single style class for one of the style dimensions. These models gener…
Emotion ClassificationStyle Transfertext-to-speechText to SpeechNeural Style Transfer and Unpaired Image-to-Image Translation to deal with the Domain Shift Problem on Spheroid Segmentation
Background and objectives. Domain shift is a generalisation problem of machine learning models that occurs when the data distribution of the training set is different to the data distribution encountered by the model whe…
Image SegmentationImage-to-Image TranslationSegmentationSemantic Segmentation+2Unpaired Motion Style Transfer from Video to Animation
Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most existing data-driven approaches are super…
3D ReconstructionMotion Style TransferMotion SynthesisStyle Transfer