# Adversarial Ranking for Language Generation

arXiv: https://arxiv.org/abs/1705.11001 · HTML: https://mlsanity.com/paper/1705.11001/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2017.

## Built on by (most cited first, 30 of 50)

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- [An improved tabular data generator with VAE-GMM integration](https://mlsanity.com/paper/2404.08434/) (2024)
- [The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models](https://mlsanity.com/paper/2108.01644/) (2021)
- [Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model](https://mlsanity.com/paper/2501.02790/) (2025)
- [LogiGAN: Learning Logical Reasoning via Adversarial Pre-training](https://mlsanity.com/paper/2205.08794/) (2022)
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- [Improved Training of Mixture-of-Experts Language GANs](https://mlsanity.com/paper/2302.11875/) (2023)
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- [Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines](https://mlsanity.com/paper/2407.21046/) (2024)
- [Generating Symbolic Reasoning Problems with Transformer GANs](https://mlsanity.com/paper/2110.10054/) (2021)
- [SocialInteractionGAN: Multi-person Interaction Sequence Generation](https://mlsanity.com/paper/2103.05916/) (2021)
- [Joint Generator-Ranker Learning for Natural Language Generation](https://mlsanity.com/paper/2206.13974/) (2022)
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## Abstract (arXiv)

Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.