---
title: "Generating Sentences from a Continuous Space — Bowman 2016"
description: "Conventional RNN language models build a sentence token by token and never form one explicit vector for the whole sentence."
authors: ["Samuel R. Bowman", "Luke Vilnis", "Oriol Vinyals", "Andrew Dai", "Rafal Jozefowicz", "Samy Bengio"]
year: 2016
venue: "Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/K16-1002/"
doi: "10.18653/v1/k16-1002"
arxiv: "1511.06349"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/"
---
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# Generating Sentences from a Continuous Space

**Authors:** [Samuel R. Bowman](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/), [Luke Vilnis](https://md.research.elamur.ai/free/en/authors/luke-vilnis/), [Oriol Vinyals](https://md.research.elamur.ai/free/en/authors/oriol-vinyals/), Andrew Dai, [Rafal Jozefowicz](https://md.research.elamur.ai/free/en/authors/rafal-jozefowicz/), [Samy Bengio](https://md.research.elamur.ai/free/en/authors/samy-bengio/) · **Year:** 2016 · **Venue:** Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/K16-1002/) · [PDF](https://aclanthology.org/K16-1002.pdf) · [DOI 10.18653/v1/k16-1002](https://doi.org/10.18653/v1/k16-1002) · [arXiv 1511.06349](https://arxiv.org/abs/1511.06349) · [OpenAlex](https://openalex.org/W2210838531)

**Citations:** 1977 citations (openalex-snapshot · 2026-10-14) · [390](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Conventional RNN language models build a sentence token by token and never form one explicit vector for the whole sentence. As a result, global properties such as topic, style or broad syntactic shape stay hidden. Bowman et al. adapt the variational autoencoder to text to address this. A single-layer LSTM encoder maps a sentence to a Gaussian latent code, and an LSTM decoder rebuilds the sentence from that code. Plain training tends to collapse: the posterior matches the prior, the KL term drops to zero, and the model acts like an ordinary RNNLM. The authors propose training techniques to prevent this, including word dropout in the decoder. They evaluate the model on Penn Treebank language modeling, on imputing missing words in a larger fiction corpus (scored with an adversarial classifier), and on TREC question classification. Perplexity is roughly the same as a standard RNNLM, so the language modeling result is negative. However, the latent variable helps with word imputation. Samples drawn from the prior and decoded deterministically are varied and grammatical. Paths between two sentences in latent space pass through plausible intermediate sentences. This showed that continuous sentence spaces can support controllable generation.

## Key points
- **Architecture:** LSTM encoder and decoder with a diagonal-Gaussian latent code, trained with the reparameterization trick. Variants with feedforward layers between the encoder, latent code and decoder only trained when 4-layer highway networks were used.
- **Alternatives tried:** More elaborate posteriors, DRAW-style multistep sampling and normalizing flows, gave no clear improvement over the plain VAE.
- **Penn Treebank hyperparameters:** These were found by Bayesian tuning over 200 runs. The standard VAE used a 13-dimensional latent code and a word-dropout keep rate of 0.62.
- **TREC Question Classification accuracy:**
  - VAE codes: 87.0
  - Sequence autoencoder: 84.2
  - CBOW: 87.3
  - Skip-thought: 91.4
  - Combine-ST: 92.2
  - VAE + combine-ST: 92.0
- **Comparison with a plain autoencoder:** Interpolating between sentence codes of a plain sequence autoencoder produces ungrammatical intermediate sentences. VAE paths stay grammatical and locally consistent in topic and vocabulary.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

390 papers of our library cite it (of the 140673 whose bibliographies we read); 25 of them have a page here, 365 not yet.

- [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/)
- [Controllable Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/)
- [A Deep Generative Framework for Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/gupta-2017-deep-generative-framework-paraphrase-generation/)
- [Generating Sentences by Editing Prototypes](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/)
- [Survey of Low-Resource Machine Translation](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/)
- [Toward Controlled Generation of Text](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)
- [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://md.research.elamur.ai/free/en/papers/kim-2020-efficient-dialogue-state-tracking-selectively/)
- [OpenNMT: Open-Source Toolkit for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
- [Adversarial Learning for Neural Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
- [Neural Net Models of Open-domain Discourse Coherence](https://md.research.elamur.ai/free/en/papers/li-2017-neural-net-models-open-domain/)
- [An efficient framework for learning sentence representations](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
- [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
- [The Linear Representation Hypothesis and the Geometry of Large Language Models](https://md.research.elamur.ai/free/en/papers/park-2023-linear-representation-hypothesis-geometry-large/)
- [Zero-Shot Text-to-Image Generation](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://md.research.elamur.ai/free/en/papers/serban-2016-hierarchical-latent-variable-encoder-decoder/)
- [Extracting Latent Steering Vectors from Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://md.research.elamur.ai/free/en/papers/ud-2019-kondratyuk-75-languages-1-model-parsing/)
- [Challenges in Data-to-Document Generation](https://md.research.elamur.ai/free/en/papers/wiseman-2017-challenges-data-document-generation/)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2018-unsupervised-text-style-transfer-using/)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://md.research.elamur.ai/free/en/papers/yoo-2021-gpt3mix-leveraging-large-scale-language/)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/)

## Cite

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

```bibtex
@inproceedings{bowman2016generating,
  title = {{Generating Sentences from a Continuous Space}},
  author = {Bowman, Samuel R. and Vilnis, Luke and Vinyals, Oriol and Dai, Andrew and Jozefowicz, Rafal and Bengio, Samy},
  booktitle = {Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning},
  year = {2016},
  doi = {10.18653/v1/k16-1002},
  eprint = {1511.06349},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/k16-1002}
}
```

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