{"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/neuralreg-an-end-to-end-approach-to-referring","title":"NeuralREG: An end-to-end approach to referring expression generation","arxiv_id":"1805.08093","date":"2018-05-21","proceeding":"ACL 2018 7","authors":["Thiago Castro Ferreira","Diego Moussallem","Ákos Kádár","Sander Wubben","Emiel Krahmer"],"abstract":"Traditionally, Referring Expression Generation (REG) models first decide on\nthe form and then on the content of references to discourse entities in text,\ntypically relying on features such as salience and grammatical function. In\nthis paper, we present a new approach (NeuralREG), relying on deep neural\nnetworks, which makes decisions about form and content in one go without\nexplicit feature extraction. Using a delexicalized version of the WebNLG\ncorpus, we show that the neural model substantially improves over two strong\nbaselines. Data and models are publicly available.","url_abs":"http://arxiv.org/abs/1805.08093v1","url_pdf":"http://arxiv.org/pdf/1805.08093v1.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":"neuralreg-an-end-to-end-approach-to-referring","repo_url":"https://github.com/ThiagoCF05/NeuralREG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"form","task_name":"Form"},{"task_slug":"referring-expression","task_name":"Referring Expression"},{"task_slug":"referring-expression-generation","task_name":"Referring expression generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.08093","atlas_url":"https://app.syntology.ai/?focus=1805.08093","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}