{"id":746566,"date":"2021-05-16T21:44:18","date_gmt":"2021-05-17T04:44:18","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=746566"},"modified":"2021-05-20T23:50:16","modified_gmt":"2021-05-21T06:50:16","slug":"tuta-tree-based-transformers-for-generally-structured-table-pre-training","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/tuta-tree-based-transformers-for-generally-structured-table-pre-training\/","title":{"rendered":"TUTA: Tree-based Transformers for Generally Structured Table Pre-training"},"content":{"rendered":"
Tables are widely used with various structures for data presentation and management. Current research works mainly focus on relational tables yet neglect other common table structures. In this paper, we propose TUTA, a unified pre-training architecture for understanding generally structured tables. Noticing that understanding a table requires spatial, hierarchical, and semantic information, so, we enhance 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