# Do sequence-to-sequence VAEs learn global features of sentences?

arXiv: https://arxiv.org/abs/2004.07683 · HTML: https://mlsanity.com/paper/2004.07683/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Step-unrolled Denoising Autoencoders for Text Generation](https://mlsanity.com/paper/2112.06749/) (2021)
- [Learning Disentangled Representations of Negation and Uncertainty](https://mlsanity.com/paper/2204.00511/) (2022)
- [Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines](https://mlsanity.com/paper/2407.21046/) (2024)
- [Exploring Story Generation with Multi-task Objectives in Variational Autoencoders](https://mlsanity.com/paper/2111.08133/) (2021)
- [Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets](https://mlsanity.com/paper/2106.03631/) (2021)
- [The Neglected Sibling: Isotropic Gaussian Posterior for VAE](https://mlsanity.com/paper/2110.07383/) (2021)

## Abstract (arXiv)

Autoregressive language models are powerful and relatively easy to train. However, these models are usually trained without explicit conditioning labels and do not offer easy ways to control global aspects such as sentiment or topic during generation. Bowman & al. (2016) adapted the Variational Autoencoder (VAE) for natural language with the sequence-to-sequence architecture and claimed that the latent vector was able to capture such global features in an unsupervised manner. We question this claim. We measure which words benefit most from the latent information by decomposing the reconstruction loss per position in the sentence. Using this method, we find that VAEs are prone to memorizing the first words and the sentence length, producing local features of limited usefulness. To alleviate this, we investigate alternative architectures based on bag-of-words assumptions and language model pretraining. These variants learn latent variables that are more global, i.e., more predictive of topic or sentiment labels. Moreover, using reconstructions, we observe that they decrease memorization: the first word and the sentence length are not recovered as accurately than with the baselines, consequently yielding more diverse reconstructions.