mlsanityData to 14 Oct 2026

VAE based Text Style Transfer with Pivot Words Enhancement Learning

arXiv 2112.03154 ↗First posted 6 Dec 2021cs.CLcited by 4

Not on the map. Why?

Abstract (arXiv)

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 has become a trending direction for TST tasks. In this paper, we propose a novel VAE based Text Style Transfer with pivOt Words Enhancement leaRning (VT-STOWER) method which utilizes Variational AutoEncoder (VAE) and external style embeddings to learn semantics and style distribution jointly. Additionally, we introduce pivot words learning, which is applied to learn decisive words for a specific style and thereby further improve the overall performance of the style transfer. The proposed VT-STOWER can be scaled to different TST scenarios given very limited and non-parallel training data with a novel and flexible style strength control mechanism. Experiments demonstrate that the VT-STOWER outperforms the state-of-the-art on sentiment, formality, and code-switching TST tasks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 33 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

26 of 33 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  5. 2018Deep contextualized word representationscited by 2,290
  6. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 941
  7. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  8. 2015Generating Sentences from a Continuous Spacecited by 612
  9. 2017Word Translation Without Parallel Datacited by 487
  10. 2017Toward Controlled Generation of Textcited by 272
  11. 2017Style Transfer from Non-Parallel Text by Cross-Alignmentcited by 248
  12. 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 211
14 more
  1. 2018Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfercited by 191
  2. 2020Deep Learning for Text Style Transfer: A Surveycited by 162
  3. 2017Style Transfer in Text: Exploration and Evaluationcited by 146
  4. 2015Character-Aware Neural Language Modelscited by 132
  5. 2018Disentangled Representation Learning for Non-Parallel Text Style Transfercited by 127
  6. 2020LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluationcited by 93
  7. 2019Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representationcited by 84
  8. 2019Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfercited by 81
  9. 2020Politeness Transfer: A Tag and Generate Approachcited by 78
  10. 2020A Probabilistic Formulation of Unsupervised Text Style Transfercited by 70
  11. 2021Consistency Regularization for Cross-Lingual Fine-Tuningcited by 41
  12. 2019A Deep Generative Model for Code-Switched Textcited by 13
  13. 2019Rethinking Text Attribute Transfer: A Lexical Analysiscited by 10
  14. 2020Robust Front-End for Multi-Channel ASR using Flow-Based Density Estimationcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2022Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Modelscited by 3
  2. 2024Task-Oriented Paraphrase Analyticscited by 1
  3. 2026COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generationcited by 1
  4. 2025Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncodercited by 0