RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training Article Swipe
Zui Chen
,
Lei Cao
,
Samuel Madden
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.11696
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.11696
We propose RoTaR, a row-based table representation learning method, to address the efficiency and scalability issues faced by existing table representation learning methods. The key idea of RoTaR is to generate query-agnostic row representations that could be re-used via query-specific aggregation. In addition to the row-based architecture, we introduce several techniques: cell-aware position embedding, teacher-student training paradigm, and selective backward to improve the performance of RoTaR model.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.11696
- https://arxiv.org/pdf/2306.11696
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381586798
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https://openalex.org/W4381586798Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.11696Digital Object Identifier
- Title
-
RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student TrainingWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-20Full publication date if available
- Authors
-
Zui Chen, Lei Cao, Samuel MaddenList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.11696Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2306.11696Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2306.11696Direct OA link when available
- Concepts
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Table (database), Computer science, Scalability, Representation (politics), Embedding, Key (lock), Training (meteorology), Theoretical computer science, Artificial intelligence, Machine learning, Data mining, Database, Physics, Meteorology, Political science, Politics, Law, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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