Methods › General › Deep Tabular Learning › TaBERT
TaBERT
Introduced by Pengcheng Yin et al. in TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
TaBERT is a pretrained language model (LM) that jointly learns representations for natural language sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts.
In summary, TaBERT's process for learning representations for NL sentences is as follows: Given an utterance u and a table T, TaBERT first creates a content snapshot of T. This snapshot consists of sampled rows that summarize the information in T most relevant to the input utterance. The model then linearizes each row in the snapshot, concatenates each linearized row with the utterance, and uses the concatenated string as input to a Transformer model, which outputs row-wise encoding vectors of utterance tokens and cells. The encodings for all the rows in the snapshot are fed into a series of vertical self-attention layers, where a cell representation (or an utterance token representation) is computed by attending to vertically-aligned vectors of the same column (or the same NL token). Finally, representations for each utterance token and column are generated from a pooling layer.
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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AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry 24 Jun 2021 · 1 repository · arXiv:2106.12944
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Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables 21 Apr 2021 · 0 repositories · arXiv:2104.10366
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TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data 17 May 2020 · 1 repository · arXiv:2005.08314
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Semantic Parsing | 3 |
| Question Answering | 2 |
| Articles | 1 |
| Form | 1 |
| Natural Language Understanding | 1 |
| Text to SQL | 1 |
| Text-To-SQL | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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