# SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation

arXiv: https://arxiv.org/abs/1804.04093 · HTML: https://mlsanity.com/paper/1804.04093/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://mlsanity.com/paper/2110.07139/) (2021)
- [A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations](https://mlsanity.com/paper/2105.02685/) (2021)
- [A Review of Text Style Transfer using Deep Learning](https://mlsanity.com/paper/2109.15144/) (2021)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [Inference Time Style Control for Summarization](https://mlsanity.com/paper/2104.01724/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Syntax Matters! Syntax-Controlled in Text Style Transfer](https://mlsanity.com/paper/2108.05869/) (2021)
- [On the Robustness of Question Rewriting Systems to Questions of Varying Hardness](https://mlsanity.com/paper/2311.06807/) (2023)
- [Topical Language Generation using Transformers](https://mlsanity.com/paper/2103.06434/) (2021)
- [Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge](https://mlsanity.com/paper/2105.11902/) (2021)
- [Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters](https://mlsanity.com/paper/2210.03264/) (2022)

## Abstract (arXiv)

Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an 'average' style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.