{"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/semantic-noise-matters-for-neural-natural","title":"Semantic Noise Matters for Neural Natural Language Generation","arxiv_id":"1911.03905","date":"2019-11-10","proceeding":"WS 2019 10","authors":["Ondřej Dušek","David M. Howcroft","Verena Rieser"],"abstract":"Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97%, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.","url_abs":"https://arxiv.org/abs/1911.03905v1","url_pdf":"https://arxiv.org/pdf/1911.03905v1.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":"semantic-noise-matters-for-neural-natural","repo_url":"https://github.com/tuetschek/e2e-cleaning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"hallucination","task_name":"Hallucination"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/data-to-text-generation-on-cleaned-e2e-nlg-1","task":"Data-to-Text Generation","dataset":"Cleaned E2E NLG Challenge","model":"TGen","rank_in_archive_order":3,"of":7,"metrics":{"BLEU (Test set)":"40.73"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.03905","atlas_url":"https://app.syntology.ai/?focus=1911.03905","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}