Tables are powerful and popular tools for organizing and manipulating data. A vast number
of tables can be found on the Web, which represent a valuable knowledge resource. The …

Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs,
and various other document types, a flurry of table pre-training frameworks have been …

We propose TUTA, a unified pre-training architecture for understanding generally structured
tables. Noticing that understanding a table requires spatial, hierarchical, and semantic …

Tables contain a significant portion of the world's structured information. The ability to
efficiently and accurately understand, process, reason about, analyze, and generate tabular …

Models pre-trained with a language modeling objective possess ample world knowledge
and language skills, but are known to struggle in tasks that require reasoning. In this work …

A desirable property of learning systems is to be both effective and interpretable. Towards
this goal, recent models have been proposed that first generate an extractive explanation …

We address the web table retrieval task, aiming to retrieve and rank web tables as whole
answers to a given information need. To this end, we formally define web tables as …

Tabular data is a way to structure, organize, and present information conveniently and
effectively. Real-world tables present data in two dimensions by arranging cells in matrices …

Information extraction from semi-structured webpages provides valuable long-tailed facts for
augmenting knowledge graph. Relational Web tables are a critical component containing …

Synthesizing tabular data is attracting much attention these days for various purposes. With
sophisticate synthetic data, for instance, one can augment its training data. For the past …