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TURL: Table Understanding through Representation Learning
TURL
Introduced by Xiang Deng et al. in TURL: Table Understanding through Representation Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work generally relies on heavily-engineered task- specific features and model architectures. In this paper, we present TURL, a novel framework that introduces the pre-training/fine- tuning paradigm to relational Web tables. During pre-training, our framework learns deep contextualized representations on relational tables in an unsupervised manner. Its universal model design with pre-trained representations can be applied to a wide range of tasks with minimal task-specific fine-tuning. Specifically, we propose a structure-aware Transformer encoder to model the row-column structure of relational tables, and present a new Masked Entity Recovery (MER) objective for pre-training to capture the semantics and knowledge in large-scale unlabeled data. We systematically evaluate TURL with a benchmark consisting of 6 different tasks for table understanding (e.g., relation extraction, cell filling). We show that TURL generalizes well to all tasks and substantially outperforms existing methods in almost all instances.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Evaluating LLMs on Entity Disambiguation in Tables 12 Aug 2024 · 0 repositories · arXiv:2408.06423
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SOTAB: The WDC Schema.org Table Annotation Benchmark 9 Jan 2023 · 1 repository
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TURL: Table Understanding through Representation Learning 26 Jun 2020 · 1 repository · arXiv:2006.14806Syntology ran 1 of 1 samples · 0 unverified
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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