# Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

arXiv: https://arxiv.org/abs/2004.10809 · HTML: https://mlsanity.com/paper/2004.10809/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Learning Disentangled Representations of Negation and Uncertainty](https://mlsanity.com/paper/2204.00511/) (2022)
- [Unsupervised Learning of Neurosymbolic Encoders](https://mlsanity.com/paper/2107.13132/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient](https://mlsanity.com/paper/2210.05367/) (2022)
- [Mitigating Negative Style Transfer in Hybrid Dialogue System](https://mlsanity.com/paper/2212.07183/) (2022)
- [A Progressive Single-Modality to Multi-Modality Classification Framework for Alzheimer's Disease Sub-type Diagnosis](https://mlsanity.com/paper/2407.18466/) (2024)
- [Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets](https://mlsanity.com/paper/2106.03631/) (2021)

## Abstract (arXiv)

Learning disentangled representations of real-world data is a challenging open problem. Most previous methods have focused on either supervised approaches which use attribute labels or unsupervised approaches that manipulate the factorization in the latent space of models such as the variational autoencoder (VAE) by training with task-specific losses. In this work, we propose polarized-VAE, an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes. We apply our method to disentangle the semantics and syntax of sentences and carry out transfer experiments. Polarized-VAE outperforms the VAE baseline and is competitive with state-of-the-art approaches, while being more a general framework that is applicable to other attribute disentanglement tasks.