{"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/totto-a-controlled-table-to-text-generation","title":"ToTTo: A Controlled Table-To-Text Generation Dataset","arxiv_id":"2004.14373","date":"2020-04-29","proceeding":"EMNLP 2020 11","authors":["Ankur P. Parikh","Xuezhi Wang","Sebastian Gehrmann","Manaal Faruqui","Bhuwan Dhingra","Diyi Yang","Dipanjan Das"],"abstract":"We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.","url_abs":"https://arxiv.org/abs/2004.14373v3","url_pdf":"https://arxiv.org/pdf/2004.14373v3.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":"totto-a-controlled-table-to-text-generation","repo_url":"https://github.com/google-research-datasets/ToTTo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"conditional-text-generation","task_name":"Conditional Text Generation"},{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"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":"totto","name":"ToTTo","full_name":"ToTTo"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/data-to-text-generation-on-totto","task":"Data-to-Text Generation","dataset":"ToTTo","model":"BERT-to-BERT","rank_in_archive_order":3,"of":6,"metrics":{"BLEU":"44","PARENT":"52.6"},"uses_additional_data":false},{"leaderboard":"/sota/data-to-text-generation-on-totto","task":"Data-to-Text Generation","dataset":"ToTTo","model":"Pointer Generator","rank_in_archive_order":4,"of":6,"metrics":{"BLEU":"41.6","PARENT":"51.6"},"uses_additional_data":false},{"leaderboard":"/sota/data-to-text-generation-on-totto","task":"Data-to-Text Generation","dataset":"ToTTo","model":"NCP+CC (Puduppully et al 2019)","rank_in_archive_order":5,"of":6,"metrics":{"BLEU":"19.2","PARENT":"29.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.14373","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}