{"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/table-pretraining-a-survey-on-model","title":"Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks","arxiv_id":"2201.09745","date":"2022-01-24","proceeding":null,"authors":["Haoyu Dong","Zhoujun Cheng","Xinyi He","Mengyu Zhou","Anda Zhou","Fan Zhou","Ao Liu","Shi Han","Dongmei Zhang"],"abstract":"Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relation classification, table search, formula prediction, etc. To fully use the supervision signals in unlabeled tables, a variety of pre-training objectives have been designed and evaluated, for example, denoising cell values, predicting numerical relationships, and implicitly executing SQLs. And to best leverage the characteristics of (semi-)structured tables, various tabular language models, particularly with specially-designed attention mechanisms, have been explored. Since tables usually appear and interact with free-form text, table pre-training usually takes the form of table-text joint pre-training, which attracts significant research interests from multiple domains. This survey aims to provide a comprehensive review of different model designs, pre-training objectives, and downstream tasks for table pre-training, and we further share our thoughts and vision on existing challenges and future opportunities.","url_abs":"https://arxiv.org/abs/2201.09745v4","url_pdf":"https://arxiv.org/pdf/2201.09745v4.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":"table-pretraining-a-survey-on-model","repo_url":"https://github.com/microsoft/TUTA_table_understanding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"table-pretraining-a-survey-on-model","repo_url":"https://github.com/microsoft/hitab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"table-pretraining-a-survey-on-model","repo_url":"https://github.com/pwc-1/Paper-10/tree/main/tapex","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"table-search","task_name":"Table Search"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.09745","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}