# Adversarial Mutual Information for Text Generation

arXiv: https://arxiv.org/abs/2007.00067 · HTML: https://mlsanity.com/paper/2007.00067/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference](https://mlsanity.com/paper/2404.13815/) (2024)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2306.07994/) (2023)
- [Learning to Accumulate Knowledge with Mutual Information](https://mlsanity.com/paper/2610.10042/) (2026)

## Abstract (arXiv)

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little attention to modeling the backward network of MI (i.e., dependency from the target to the source), which is crucial to the tightness of the variational information maximization lower bound. In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target. Within this framework, the forward and backward networks are able to iteratively promote or demote each other's generated instances by comparing the real and synthetic data distributions. We also develop a latent noise sampling strategy that leverages random variations at the high-level semantic space to enhance the long term dependency in the generation process. Extensive experiments based on different text generation tasks demonstrate that the proposed AMI framework can significantly outperform several strong baselines, and we also show that AMI has potential to lead to a tighter lower bound of maximum mutual information for the variational information maximization problem.