---
title: "Toward Controlled Generation of Text — Hu 2017"
description: "Neural text generators based on VAEs or GANs usually produce sentences whose properties are random and cannot be steered."
authors: ["Zhiting Hu", "Zichao Yang", "Xiaodan Liang", "Ruslan Salakhutdinov", "Eric P. Xing"]
year: 2017
venue: "arXiv:1703.00955"
source: "https://arxiv.org/abs/1703.00955"
doi: "10.48550/arxiv.1703.00955"
arxiv: "1703.00955"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/"
---
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# Toward Controlled Generation of Text

**Authors:** [Zhiting Hu](https://md.research.elamur.ai/free/en/authors/zhiting-hu/), [Zichao Yang](https://md.research.elamur.ai/free/en/authors/zichao-yang/), [Xiaodan Liang](https://md.research.elamur.ai/free/en/authors/xiaodan-liang/), [Ruslan Salakhutdinov](https://md.research.elamur.ai/free/en/authors/ruslan-salakhutdinov/), [Eric P. Xing](https://md.research.elamur.ai/free/en/authors/eric-p-xing/) · **Year:** 2017 · **Venue:** arXiv:1703.00955 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1703.00955) · [PDF](https://arxiv.org/pdf/1703.00955) · [DOI 10.48550/arxiv.1703.00955](https://doi.org/10.48550/arxiv.1703.00955) · [arXiv 1703.00955](https://arxiv.org/abs/1703.00955) · [OpenAlex](https://openalex.org/W2735642330)

**Citations:** 119 citations (openalex-snapshot · 2026-10-14) · [394](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Neural text generators based on VAEs or GANs usually produce sentences whose properties are random and cannot be steered. Two obstacles cause this: discrete tokens block gradients from sentence-level critics, and latent codes tend to be entangled. Hu et al. split the latent space into an unstructured vector z and a structured code c. Each element of c is assigned one attribute, such as sentiment or tense. A VAE decoder is trained alongside per-attribute classifiers that judge whole generated sentences. Gradients reach the decoder through an annealed softmax relaxation of word choices. The encoder is reused to check that z survives changes to c, which limits leakage between attributes. Training follows an extended wake-sleep scheme in which synthetic sentences become extra labeled data for both generator and classifiers. Each classifier can be trained on its own dataset, so attributes never annotated together can still be combined. On short movie-review sentences, the model controls sentiment and tense using little supervision. This made it an early, influential step toward controllable text generation and style transfer.

## Key points
- **Architecture:** a VAE is extended with holistic attribute discriminators. An independency constraint (Eq. 7) uses the encoder to keep unspecified features fixed when an attribute code is changed. Removing this constraint visibly degrades disentanglement in the paper's examples.
- **Training:** a continuous softmax approximation, annealed toward discrete outputs, makes end-to-end learning possible. An extended sleep phase updates both the generator and the discriminators using generated samples.
- **Data and supervision:** no corpus labels sentiment and tense together, so the authors use separate datasets. SST-full serves for sentiment, and tense is learned from word-level labels only. Roughly hundreds of annotated sentences suffice.
- **Evaluation:** pretrained classifiers act as judges. The model beats earlier generative models on attribute accuracy, and its generated samples also improve classifiers as augmentation data. The authors report that output is limited to short sentences and show some failure cases.
- **Publication and code:** the paper appeared at ICML 2017 (PMLR 70). An adapted implementation is available in the Texar toolkit under `examples/text_style_transfer`.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

394 papers of our library cite it (of the 140884 whose bibliographies we read); 42 of them have a page here, 352 not yet.

- [The AraGenEval Shared Task on Arabic Authorship Style Transfer and AI Generated Text Detection](https://md.research.elamur.ai/free/en/papers/abudalfa-2025-arageneval-shared-task-arabic-authorship/)
- [Evaluating prose style transfer with the Bible](https://md.research.elamur.ai/free/en/papers/carlson-2017-evaluating-prose-style-transfer-bible/)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://md.research.elamur.ai/free/en/papers/chen-2019-controllable-paraphrase-generation-syntactic-exemplar/)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/)
- [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://md.research.elamur.ai/free/en/papers/dai-2019-style-transformer-unpaired-text-style/)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/)
- [Controllable Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/)
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- [Style Transfer Through Back-Translation](https://md.research.elamur.ai/free/en/papers/prabhumoye-2018-style-transfer-through-back-translation/)
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- [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/qin-2017-adversarial-connective-exploiting-networks-implicit/)
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- [Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer](https://md.research.elamur.ai/free/en/papers/santos-2018-fighting-offensive-language-social-media/)
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- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://md.research.elamur.ai/free/en/papers/shen-2017-style-transfer-non-parallel-text/)
- [Towards Controllable Biases in Language Generation](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/)
- [AMR-TST: Abstract Meaning Representation-based Text Style Transfer](https://md.research.elamur.ai/free/en/papers/shi-2023-amr-tst-abstract-meaning-representation/)
- [Extracting Latent Steering Vectors from Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/)
- [“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer](https://md.research.elamur.ai/free/en/papers/sudhakar-2019-transforming-delete-retrieve-generate-approach/)
- [HCMUS_The Fangs at AbjadStyleTransfer Shared Task: Learning to Query Style, Contrastive Representations for Zero-Shot Arabic Authorship Style Transfer](https://md.research.elamur.ai/free/en/papers/sy-2026-hcmus-fangs-abjadstyletransfer-shared-task/)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://md.research.elamur.ai/free/en/papers/wei-2019-eda-easy-data-augmentation-techniques/)
- [Learning Neural Templates for Text Generation](https://md.research.elamur.ai/free/en/papers/wiseman-2018-learning-neural-templates-text-generation/)
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## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{hu2017toward,
  title = {{Toward Controlled Generation of Text}},
  author = {Hu, Zhiting and Yang, Zichao and Liang, Xiaodan and Salakhutdinov, Ruslan and Xing, Eric P.},
  year = {2017},
  doi = {10.48550/arxiv.1703.00955},
  eprint = {1703.00955},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1703.00955}
}
```

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