# SALSA-TEXT : self attentive latent space based adversarial text generation

arXiv: https://arxiv.org/abs/1809.11155 · HTML: https://mlsanity.com/paper/1809.11155/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making](https://mlsanity.com/paper/2305.17440/) (2023)
- [Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?](https://mlsanity.com/paper/2108.12275/) (2021)
- [TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models](https://mlsanity.com/paper/2408.13985/) (2024)

## Abstract (arXiv)

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code- based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to their promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.