OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering

Understand

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 often adds an extra burden to both model design and data annotation.
• In this paper, we aim to develop a simple table-based QA model with minimal annotation effort.
• Motivated by the fact that table-based QA requires both alignment between questions and tables and the ability to perform complicated reasoning over multiple table elements, we propose an omnivorous pretraining approach that consumes both natural and synthetic data to endow models with these respective abilities.