Table Pre-training: A Survey on Model Architectures, Pretraining Objectives, and Downstream Tasks

• URL: http://arxiv.org/abs/2201.09745v2
• Date: Thu, 27 Jan 2022 03:41:50 GMT
• Title: Table Pre-training: A Survey on Model Architectures, Pretraining Objectives, and Downstream Tasks
• Authors: Haoyu Dong, Zhoujun Cheng, Xinyi He, Mengyu Zhou, Anda Zhou, Fan Zhou, Ao Liu, Shi Han, Dongmei Zhang
• Abstract summary: A flurry of table pre-training frameworks have been proposed following the success of text and images. Table pre-training usually takes the form of table-text joint pre-training. This survey aims to provide a comprehensive review of different model designs, pre-training objectives, and downstream tasks for table pre-training.
• Score: 37.35651138851127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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