  Existing question answering systems mainly focus on dealing with text data.
However, much of the data produced daily is stored in the form of tables that
can be found in documents and relational databases, or on the web. To solve the
task of question answering over tables, there exist many datasets for table
question answering written in English, but few Korean datasets. In this paper,
we demonstrate how we construct Korean-specific datasets for table question
answering: Korean tabular dataset is a collection of 1.4M tables with
corresponding descriptions for unsupervised pre-training language models.
Korean table question answering corpus consists of 70k pairs of questions and
answers created by crowd-sourced workers. Subsequently, we then build a
pre-trained language model based on Transformer and fine-tune the model for
table question answering with these datasets. We then report the evaluation
results of our model. We make our datasets publicly available via our GitHub
repository and hope that those datasets will help further studies for question
answering over tables, and for the transformation of table formats.
