Papers with retrieval augmented transformer model

1 papers
Retrieval-Based Transformer for Table Augmentation (2023.findings-acl)

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Challenge: Data preparation is one of the most expensive and time-consuming steps when performing analytics or building machine learning models.
Approach: They propose a retrieval augmented transformer model that is self-trained for table augmentation tasks.
Outcome: The proposed model outperforms current state-of-the-art models on EntiTables and WebTables.

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