{"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/neural-text-generation-from-structured-data","title":"Neural Text Generation from Structured Data with Application to the Biography Domain","arxiv_id":"1603.07771","date":"2016-03-24","proceeding":"EMNLP 2016 11","authors":["Remi Lebret","David Grangier","Michael Auli"],"abstract":"This paper introduces a neural model for concept-to-text generation that\nscales to large, rich domains. We experiment with a new dataset of biographies\nfrom Wikipedia that is an order of magnitude larger than existing resources\nwith over 700k samples. The dataset is also vastly more diverse with a 400k\nvocabulary, compared to a few hundred words for Weathergov or Robocup. Our\nmodel builds upon recent work on conditional neural language model for text\ngeneration. To deal with the large vocabulary, we extend these models to mix a\nfixed vocabulary with copy actions that transfer sample-specific words from the\ninput database to the generated output sentence. Our neural model significantly\nout-performs a classical Kneser-Ney language model adapted to this task by\nnearly 15 BLEU.","url_abs":"http://arxiv.org/abs/1603.07771v3","url_pdf":"http://arxiv.org/pdf/1603.07771v3.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":"neural-text-generation-from-structured-data","repo_url":"https://github.com/nathanlesage/so-classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"neural-text-generation-from-structured-data","repo_url":"https://github.com/parajain/data-to-text","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"concept-to-text-generation","task_name":"Concept-To-Text Generation"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"table-to-text-generation","task_name":"Table-to-Text Generation"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[{"slug":"wikibio","name":"WikiBio","full_name":"Wikipedia Biography Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/table-to-text-generation-on-wikibio","task":"Table-to-Text Generation","dataset":"WikiBio","model":"Table NLM","rank_in_archive_order":4,"of":4,"metrics":{"BLEU":"34.70","ROUGE":"25.80"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.07771","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}