Papers › TURL: Table Understanding through Representation Learning
TURL: Table Understanding through Representation Learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, Cong Yu
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.
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9ad8fa09f0972efd · report
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cell Entity Annotation | WikiTables-TURL-CEA | TURL | F1 (%) | 68 | #1 of 1 | Archive leaderboard | report |
| Cell Entity Annotation | WikipediaGS | TURL | F1 (%) | 67 | #1 of 1 | Archive leaderboard | report |
| Column Type Annotation | T2Dv2 | TURL | Accuracy (%) | 96.2 | #2 of 3 | Archive leaderboard | report |
| Column Type Annotation | WikiTables-TURL-CTA | TURL | F1 (%) | 94.75 | #1 of 3 | Archive leaderboard | report |
| Column Type Annotation | WikipediaGS-CTA | TURL | Accuracy (%) | 74.6 | #1 of 2 | Archive leaderboard | report |
| Columns Property Annotation | WikiTables-TURL-CPA | TURL | F1 (%) | 94.91 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: TURL
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