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arXiv · 2306.11696

RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training

Zui Chen·Lei Cao·Sam Madden
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Abstract

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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Zui Chen, Lei Cao, Sam Madden. 2023-06-20. RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training. https://arxiv.org/abs/2306.11696

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