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.

翻译：数据准备（也称为数据整理）在数据分析或构建机器学习模型时被认为是最昂贵且最耗时的步骤之一。数据准备通常涉及从复杂异构、且常为大规模的数据源（如数据湖）中收集和合并数据。本文提出了一种面向自动数据整理的新方法，旨在减轻最终用户（如数据分析师）从数据湖中以表格数据形式构建动态视图的工作量。我们致力于解决表格增广任务，包括行/列填充和缺失值填补。给定一个表格语料库，我们提出了一种基于检索增强的自训练Transformer模型。我们的自学习策略是：从语料库中随机删除表格的部分内容，并以部分表格作为输入，训练基于检索的模型重建原始值或表头。我们采用此策略首先训练稠密神经检索模型（将表格片段编码为向量），进而训练端到端模型以执行表格增广任务。我们在表格增广的标准基准数据集EntiTables上进行了测试，同时引入新基准数据集WebTables以推动进一步研究。我们的模型在性能上持续且显著优于有监督统计方法以及当前最先进的基于Transformer的模型。