{"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/generative-adversarial-networks-for-text","title":"Generative Adversarial Networks for text using word2vec intermediaries","arxiv_id":"1904.02293","date":"2019-04-04","proceeding":"WS 2019 8","authors":["Akshay Budhkar","Krishnapriya Vishnubhotla","Safwan Hossain","Frank Rudzicz"],"abstract":"Generative adversarial networks (GANs) have shown considerable success,\nespecially in the realistic generation of images. In this work, we apply\nsimilar techniques for the generation of text. We propose a novel approach to\nhandle the discrete nature of text, during training, using word embeddings. Our\nmethod is agnostic to vocabulary size and achieves competitive results relative\nto methods with various discrete gradient estimators.","url_abs":"http://arxiv.org/abs/1904.02293v1","url_pdf":"http://arxiv.org/pdf/1904.02293v1.pdf","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":"generative-adversarial-networks-for-text","repo_url":"https://github.com/adventure2165/GAN2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}