{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/relgan-relational-generative-adversarial","title":"RelGAN: Relational Generative Adversarial Networks for Text Generation","arxiv_id":null,"date":"2019-05-01","proceeding":"ICLR 2019 5","authors":["Weili Nie","Nina Narodytska","Ankit Patel"],"abstract":"Generative adversarial networks (GANs) have achieved great success at generating realistic images. However, the text generation still remains a challenging task for modern GAN architectures. In this work, we propose RelGAN, a new GAN architecture for text generation, consisting of three main components: a relational memory based generator for the long-distance dependency modeling, the Gumbel-Softmax relaxation for training GANs on discrete data, and multiple embedded representations in the discriminator to provide a more informative signal for the generator updates. Our experiments show that RelGAN outperforms current state-of-the-art models in terms of sample quality and diversity, and we also reveal via ablation studies that each component of RelGAN contributes critically to its performance improvements. Moreover, a key advantage of our method, that distinguishes it from other GANs, is the ability to control the trade-off between sample quality and diversity via the use of a single adjustable parameter. Finally, RelGAN is the first architecture that makes GANs with Gumbel-Softmax relaxation succeed in generating realistic text.","url_abs":"https://openreview.net/forum?id=rJedV3R5tm","url_pdf":"https://openreview.net/pdf?id=rJedV3R5tm","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"relgan-relational-generative-adversarial","repo_url":"https://github.com/weilinie/RelGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-generation-on-coco-captions","task":"Text Generation","dataset":"COCO Captions","model":"RelGAN (100)","rank_in_archive_order":4,"of":5,"metrics":{"BLEU-2":"0.849","BLEU-3":"0.687","BLEU-4":"0.502"},"uses_additional_data":false},{"leaderboard":"/sota/text-generation-on-emnlp2017-wmt","task":"Text Generation","dataset":"EMNLP2017 WMT","model":"RelGAN","rank_in_archive_order":3,"of":5,"metrics":{"BLEU-2":"0.881","BLEU-3":"0.705","BLEU-4":"0.501","BLEU-5":"0.319"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}