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 …

Tabular data (or tables) are the most widely used data format in machine learning (ML).
However, ML models often assume the table structure keeps fixed in training and testing …

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such
tables, there has been tremendous progress on a variety of tasks in the area of table …

In the last few years, the natural language processing community witnessed advances in
neural representations of free texts with transformer-based language models (LMs). Given …

In the last few years, the natural language processing community has witnessed advances
in neural representations of free texts with transformer-based language models (LMs). Given …

Tables are often created with hierarchies, but existing works on table reasoning mainly focus
on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods …

Abstract Language models pretrained on large collections of tabular data have
demonstrated their effectiveness in several downstream tasks. However, many of these …

Controlled table-to-text generation seeks to generate natural language descriptions for
highlighted subparts of a table. Previous SOTA systems still employ a sequence-to …

A table is composed of data values that are organized in rows and columns providing
implicit structural information. A table is usually accompanied by secondary information such …

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery
has become an important operation in data lake management. Existing approaches target …