  Tabular data is difficult to analyze and to search through, yielding for new
tools and interfaces that would allow even non tech-savvy users to gain
insights from open datasets without resorting to specialized data analysis
tools or even without having to fully understand the dataset structure. The
goal of our demonstration is to showcase answering natural language questions
from tabular data, and to discuss related system configuration and model
training aspects. Our prototype is publicly available and open-sourced (see
https://svakulenko.ai.wu.ac.at/tableqa).
