基于检索的Transformer用于表格增强
Retrieval-Based Transformer for Table Augmentation
AI总结：
提出检索增强自训练Transformer模型，通过随机消融表格自监督学习，解决表格增强任务，在EntiTables和WebTables基准上显著优于现有方法。
摘要：
数据准备，也称为数据整理，在执行分析或构建机器学习模型时被认为是最昂贵和最耗时的步骤之一。准备数据通常涉及从复杂异构且通常是大规模的数据源（如数据湖）中收集和合并数据。在本文中，我们介绍了一种新颖的自动数据整理方法，旨在减轻最终用户（如数据分析师）在从数据湖中以表格数据形式构建动态视图时的负担。我们旨在解决表格增强任务，包括行/列填充和数据插补。给定一个表格语料库，我们提出了一种检索增强的自训练Transformer模型。我们的自学习策略包括从语料库中随机消融表格，并训练基于检索的模型以在给定部分表格作为输入的情况下重建原始值或表头。我们采用此策略首先训练将表格部分编码为向量的密集神经检索模型，然后训练端到端模型以执行表格增强任务。我们在EntiTables（表格增强的标准基准）上进行了测试，并引入了一个新基准以推动进一步研究：WebTables。我们的模型始终且显著优于监督统计方法和当前最先进的基于Transformer的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
