Retrieval-Based Transformer for Table Augmentation

• URL: http://arxiv.org/abs/2306.11843v1
• Date: Tue, 20 Jun 2023 18:51:21 GMT
• Title: Retrieval-Based Transformer for Table Augmentation
• Authors: Michael Glass, Xueqing Wu, Ankita Rajaram Naik, Gaetano Rossiello, Alfio Gliozzo
• Abstract summary: We introduce a novel approach toward automatic data wrangling. We aim to address table augmentation tasks, including row/column population and data imputation. Our model consistently and substantially outperforms both supervised statistical methods and the current state-of-the-art transformer-based models.
• Score: 14.460363647772745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data preparation, also called data wrangling, is considered one of the most expensive and time-consuming steps when performing analytics or building machine learning models. Preparing data typically involves collecting and merging data from complex heterogeneous, and often large-scale data sources, such as data lakes. In this paper, we introduce a novel approach toward automatic data wrangling in an attempt to alleviate the effort of end-users, e.g. data analysts, in structuring dynamic views from data lakes in the form of tabular data. We aim to address table augmentation tasks, including row/column population and data imputation. Given a corpus of tables, we propose a retrieval augmented self-trained transformer model. Our self-learning strategy consists in randomly ablating tables from the corpus and training the retrieval-based model to reconstruct the original values or headers given the partial tables as input. We adopt this strategy to first train the dense neural retrieval model encoding table-parts to vectors, and then the end-to-end model trained to perform table augmentation tasks. We test on EntiTables, the standard benchmark for table augmentation, as well as introduce a new benchmark to advance further research: WebTables. Our model consistently and substantially outperforms both supervised statistical methods and the current state-of-the-art transformer-based models.

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