Tabular data often refers to data that is organized in a table with rows and columns. We
observe that this data format is widely used on the Web and within enterprise data …

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 …

Recent progress in language model pre-training has achieved a great success via
leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre …

Transformers are widely used deep learning architectures. Existing transformers are mostly
designed for sequences (texts or time series), images or videos, and graphs. This paper …

Numerical reasoning over hybrid data containing both textual and tabular content (eg,
financial reports) has recently attracted much attention in the NLP community. However …

Inferring meta information about tables, such as column headers or relationships between
columns, is an active research topic in data management as we find many tables are …

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 …

The information in tables can be an important complement to text, making table-based
question answering (QA) systems of great value. The intrinsic complexity of handling tables …

Table-based reasoning with large language models (LLMs) is a promising direction to tackle
many table understanding tasks, such as table-based question answering and fact …

Semi-structured tables are ubiquitous. There has been a variety of tasks that aim to
automatically interpret, augment, and query tables. Current methods often require …