# TableQA: Question Answering on Tabular Data

Source: [TableQA: Question Answering on Tabular Data](<https://www.sota2.com/research/paper/68fa639b1e209131ce9ff8a1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:44:17.219Z

Authors: Svitlana Vakulenko, Vadim Savenkov

Published: 2017-05-18T10:08:38.000Z

## Abstract

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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1705.06504>)

[PDF](<https://arxiv.org/pdf/1705.06504.pdf>)