  Question-answering (QA) on hybrid scientific tabular and textual data deals
with scientific information, and relies on complex numerical reasoning. In
recent years, while tabular QA has seen rapid progress, understanding their
robustness on scientific information is lacking due to absence of any benchmark
dataset. To investigate the robustness of the existing state-of-the-art QA
models on scientific hybrid tabular data, we propose a new dataset, "SciTabQA",
consisting of 822 question-answer pairs from scientific tables and their
descriptions. With the help of this dataset, we assess the state-of-the-art
Tabular QA models based on their ability (i) to use heterogeneous information
requiring both structured data (table) and unstructured data (text) and (ii) to
perform complex scientific reasoning tasks. In essence, we check the capability
of the models to interpret scientific tables and text. Our experiments show
that "SciTabQA" is an innovative dataset to study question-answering over
scientific heterogeneous data. We benchmark three state-of-the-art Tabular QA
models, and find that the best F1 score is only 0.462.
