TableFormer: Robust Transformer Modeling for Table-Text Encoding
Open accessAbstract
Understanding tables is an important aspect of natural language understanding.Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.Such spurious biases make the model vulnerable to row and column order perturbations.Additionally, prior work has not thoroughly modeled the table structures or table-text alignments, hindering the table-text understanding ability.In this work, we propose a robust and structurally aware table-text encoding architecture TABLEFORMER, where tabular structural biases are incorporated completely through learnable attention biases.TABLEFORMER is (1) strictly invariant to row and column orders, and, (2) could understand tables better due to its tabular inductive biases.Our evaluations showed that TABLEFORMER outperforms strong baselines in all settings on SQA, WTQ and TABFACT table reasoning datasets, and achieves state-of-the-art performance on SQA, especially when facing answer-invariant row and column order perturbations (6% improvement over the best baseline), because previous SOTA models' performance drops by 4% -6% when facing such perturbations while TABLEFORMER is not affected.1
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The shortcut learning problem has been observed in various natural language understanding tasks under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and labels in the fine-tuning data for prediction.
“The shortcut learning problem has been observed in various natural language understanding tasks under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and labels in the fine-tuning data for prediction [35,39,107,109,128,129].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [128]
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- Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang. 2021. TUTA: Tree-based Transformers for Generally Structured Table Pre-training. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1780-1790.
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KOreaPAS: TAPAS based Korean-Specific Table Question Answering Model
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Tabular reasoning via two-stage knowledge injection
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- Authors
- Yang, J., Gupta, A., Upadhyay, S., He, L., Goel, R., & Paul, S.
- Affiliations
- Google (United States) (US)
- Georgia Institute of Technology (US)
- Corresponding author
- Yang, Jingfeng
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 528-537
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4221163895
- Cited by
- 85
- References
- 21
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 17 · 2023: 33 · 2022: 13
- FWCI
- 10.65
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- table understanding, table reasoning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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